Apparatus and method for bounding volume hierarchy (bvh) construction with stochastic processing

ABSTRACT

A method and apparatus for efficiently constructing a bounding volume hierarchy (BVH). For example, one embodiment of an apparatus comprises: a primitive sampler to identify a representative subset of input primitives of a graphics scene; bounding volume hierarchy (BVH) builder hardware logic to construct an approximate BVH based on the representative subset of input primitives; hardware logic to insert input primitives not in the representative subset into leaves of the approximate BVH; and the BVH builder or a different BVH builder to construct a final BVH based on the primitives inserted into the leaves of the approximate BVH.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 63/343,356, filed May 18, 2022, all of which isherein incorporated by reference.

BACKGROUND Field of the Invention

This invention relates generally to the field of graphics processors.More particularly, the invention relates to an apparatus and method forBVH construction with stochastic processing.

Description of the Related Art

Ray tracing is a technique in which a light transport is simulatedthrough physically-based rendering. Widely used in cinematic rendering,it was considered too resource-intensive for real-time performance untiljust a few years ago. One of the key operations in ray tracing isprocessing a visibility query for ray-scene intersections known as “raytraversal” which computes ray-scene intersections by traversing andintersecting nodes in a bounding volume hierarchy (BVH).

Rasterization is a technique in which, screen objects are created from3D models of objects created from a mesh of triangles. The vertices ofeach triangle intersect with the vertices of other triangles ofdifferent shapes and sizes. Each vertex has a position in space as wellas information about color, texture and its normal, which is used todetermine the way the surface of an object is facing. A rasterizationunit converts the triangles of the 3D models into pixels in a 2D screenspace and each pixel can be assigned an initial color value based on thevertex data.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present invention can be obtained from thefollowing detailed description in conjunction with the followingdrawings, in which:

FIG. 1 is a block diagram of a processing system, according to anembodiment.

FIG. 2A is a block diagram of an embodiment of a processor having one ormore processor cores, an integrated memory controller, and an integratedgraphics processor.

FIG. 2B is a block diagram of hardware logic of a graphics processorcore block, according to some embodiments described herein.

FIG. 2C illustrates a graphics processing unit (GPU) that includesdedicated sets of graphics processing resources arranged into multi-coregroups.

FIG. 2D is a block diagram of general-purpose graphics processing unit(GPGPU) that can be configured as a graphics processor and/or computeaccelerator, according to embodiments described herein.

FIG. 3A is a block diagram of a graphics processor, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores, or other semiconductordevices such as, but not limited to, memory devices or networkinterfaces.

FIG. 3B illustrates a graphics processor having a tiled architecture,according to embodiments described herein.

FIG. 3C illustrates a compute accelerator, according to embodimentsdescribed herein.

FIG. 4 is a block diagram of a graphics processing engine of a graphicsprocessor in accordance with some embodiments.

FIG. 5A illustrates graphics core cluster, according to an embodiment.

FIG. 5B illustrates a vector engine of a graphics core, according to anembodiment.

FIG. 5C illustrates a matrix engine of a graphics core, according to anembodiment.

FIG. 6 illustrates a tile of a multi-tile processor, according to anembodiment.

FIG. 7 is a block diagram illustrating graphics processor instructionformats according to some embodiments.

FIG. 8 is a block diagram of another embodiment of a graphics processor.

FIG. 9A is a block diagram illustrating a graphics processor commandformat that may be used to program graphics processing pipelinesaccording to some embodiments.

FIG. 9B is a block diagram illustrating a graphics processor commandsequence according to an embodiment.

FIG. 10 illustrates an exemplary graphics software architecture for adata processing system according to some embodiments.

FIG. 11A is a block diagram illustrating an IP core development systemthat may be used to manufacture an integrated circuit to performoperations according to an embodiment.

FIG. 11B illustrates a cross-section side view of an integrated circuitpackage assembly 1170, according to some embodiments described herein.

FIG. 11C illustrates a package assembly that includes multiple units ofhardware logic chiplets connected to a substrate.

FIG. 11D illustrates a package assembly including interchangeablechiplets, according to an embodiment.

FIG. 12 is a block diagram illustrating an exemplary system on a chipintegrated circuit that may be fabricated using one or more IP cores,according to an embodiment.

FIG. 13 illustrates an exemplary graphics processor of a system on achip integrated circuit that may be fabricated using one or more IPcores, according to an embodiment.

FIG. 14 illustrates an additional exemplary graphics processor 1340 of asystem on a chip integrated circuit that may be fabricated using one ormore IP cores, according to an embodiment;

FIG. 15 illustrates an architecture for performing initial training of amachine-learning architecture;

FIG. 16 illustrates how a machine-learning engine is continually trainedand updated during runtime;

FIG. 17 illustrates how a machine-learning engine is continually trainedand updated during runtime;

FIGS. 18A-B illustrate how machine learning data is shared on a network;and

FIG. 19 illustrates a method for training a machine-learning engine;

FIG. 20 illustrates how nodes exchange ghost region data to performdistributed denoising operations;

FIG. 21 illustrates an architecture in which image rendering anddenoising operations are distributed across a plurality of nodes;

FIG. 22 illustrates additional details of an architecture fordistributed rendering and denoising;

FIG. 23 illustrates a method for performing distributed rendering anddenoising;

FIG. 24 illustrates a machine learning method;

FIG. 25 illustrates a plurality of interconnected general purposegraphics processors;

FIG. 26 illustrates a set of convolutional layers and fully connectedlayers for a machine learning implementation;

FIG. 27 illustrates an example of a convolutional layer;

FIG. 28 illustrates an example of a set of interconnected nodes in amachine learning implementation;

FIG. 29 illustrates a training framework within which a neural networklearns using a training dataset;

FIG. 30A illustrates examples of model parallelism and data parallelism;

FIG. 30B illustrates a system on a chip (SoC);

FIG. 31 illustrates a processing architecture which includes ray tracingcores and tensor cores;

FIG. 32 illustrates an example of a beam;

FIG. 33 illustrates an apparatus for performing beam tracing;

FIG. 34 illustrates an example of a beam hierarchy;

FIG. 35 illustrates a method for performing beam tracing;

FIG. 36 illustrates an example of a distributed ray tracing engine;

FIGS. 37-38 illustrate compression performed in a ray tracing system;

FIG. 39 illustrates a method implemented on a ray tracing architecture;

FIG. 40 illustrates an exemplary hybrid ray tracing apparatus;

FIG. 41 illustrates stacks used for ray tracing operations;

FIG. 42 illustrates additional details for a hybrid ray tracingapparatus;

FIG. 43 illustrates a bounding volume hierarchy;

FIG. 44 illustrates a call stack and traversal state storage;

FIG. 45 illustrates a method for traversal and intersection;

FIGS. 46A-B illustrate how multiple dispatch cycles are required toexecute certain shaders;

FIG. 47 illustrates how a single dispatch cycle executes a plurality ofshaders;

FIG. 48 illustrates how a single dispatch cycle executes a plurality ofshaders;

FIG. 49 illustrates an architecture for executing ray tracinginstructions;

FIG. 50 illustrates a method for executing ray tracing instructionswithin a thread;

FIG. 51 illustrates one embodiment of an architecture for asynchronousray tracing;

FIG. 52A illustrates one embodiment of a ray traversal circuit;

FIG. 52B illustrates processes executed in one embodiment to manage raystorage banks;

FIG. 53 illustrates one embodiment of priority selectioncircuitry/logic;

FIGS. 54 and 55A-B illustrate different types of ray tracing dataincluding flags, exceptions, and culling data used in one embodiment ofthe invention;

FIG. 56 illustrates one embodiment for determining early out of the raytracing pipeline;

FIG. 57 illustrates one embodiment of priority selectioncircuitry/logic;

FIG. 58 illustrates an example bounding volume hierarchy (BVH) used forray traversal operations;

FIGS. 59A-B illustrate additional traversal operations;

FIG. 60 illustrates one embodiment of stack management circuitry formanaging a BVH stack;

FIGS. 61A-B illustrate example data structures, sub-structures, andoperations performed for rays, hits, and stacks;

FIG. 62 illustrates an embodiment of a level of detail selector with anN-bit comparison operation mask;

FIG. 63 illustrates an acceleration data structure in accordance withone embodiment of the invention;

FIG. 64 illustrates one embodiment of a compression block includingresidual values and metadata;

FIG. 65 illustrates a method in accordance with one embodiment of theinvention;

FIG. 66 illustrates one embodiment of a block offset index compressionblock;

FIG. 67A illustrates a Hierarchical Bit-Vector Indexing (HBI) inaccordance with one embodiment of the invention;

FIG. 67B illustrates an index compression block in accordance with oneembodiment of the invention; and

FIG. 68 illustrates an example architecture including BVH compressioncircuitry/logic and decompression circuitry/logic.

FIG. 69A illustrates a displacement function applied to a mesh;

FIG. 69B illustrates one embodiment of compression circuitry forcompressing a mesh or meshlet;

FIG. 70A illustrates displacement mapping on a base subdivision surface;

FIGS. 70B-C illustrates difference vectors relative to a coarse basemesh;

FIG. 71 illustrates a method in accordance with one embodiment of theinvention;

FIGS. 72-74 illustrate a mesh comprising a plurality of interconnectedvertices;

FIG. 75 illustrates one embodiment of a tesselator for generating amesh;

FIGS. 76-77 illustrates one embodiment in which bounding volumes areformed based on a mesh;

FIG. 78 illustrates one embodiment of a mesh sharing overlappingvertices;

FIG. 79 illustrates a mesh with shared edges between triangles;

FIG. 80 illustrates a ray tracing engine in accordance with oneembodiment;

FIG. 81 illustrate a BVH compressor in accordance with one embodiment;

FIGS. 82A-C illustrate example data formats for a 64-bit register;

FIGS. 83A-B illustrate one embodiment of an index for a ring buffer;

FIG. 84A-B illustrate example ring buffer atomics for producers andconsumers;

FIG. 85A illustrates one embodiment of a tiled resource;

FIG. 85B illustrates a method in accordance with one embodiment of theinvention;

FIG. 86A illustrates one embodiment of BVH processing logic including anon-demand builder;

FIG. 86B illustrates one embodiment of an on-demand builder for anacceleration structure;

FIG. 86C illustrates one embodiment of a visible bottom levelacceleration structure map;

FIG. 86D illustrates different types of instances and traversaldecisions;

FIG. 87 illustrates one embodiment of a material-based cull mask;

FIG. 88 illustrates one embodiment in which a quadtree structure isformed over a geometry mesh;

FIG. 89A illustrates one embodiment of a ray tracing architecture;

FIG. 89B illustrates one embodiment which includes meshlet compression;

FIG. 90 illustrates a plurality of threads including synchronousthreads, diverging spawn threads, regular spawn threads, and convergingspawn threads;

FIG. 91 illustrates one embodiment of a ray tracing architecture with abindless thread dispatcher;

FIG. 92 illustrates a ray tracing cluster in accordance with oneembodiment;

FIG. 93-100 illustrate embodiments of using proxy data in a multi-noderay tracing implementation;

FIG. 101 illustrates a method in accordance with one embodiment of theinvention;

FIG. 102 illustrates a comparison of various builders with regards tobuild time, quality and ray tracing performance;

FIG. 103A-B illustrate an analysis of construction time speed-uprelative to a top down builder;

FIG. 104 illustrates an apparatus and method in accordance withembodiments of the invention; and

FIG. 105 illustrates associations between spatially-ordered primitives,primitive subsets, and leaf nodes in accordance with embodiments of theinvention.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the embodiments of the invention described below. Itwill be apparent, however, to one skilled in the art that theembodiments of the invention may be practiced without some of thesespecific details. In other instances, well-known structures and devicesare shown in block diagram form to avoid obscuring the underlyingprinciples of the embodiments of the invention.

Exemplary Graphics Processor Architectures and Data Types

System Overview

FIG. 1 is a block diagram of a processing system 100, according to anembodiment. System 100 may be used in a single processor desktop system,a multiprocessor workstation system, or a server system having a largenumber of processors 102 or processor cores 107. In one embodiment, thesystem 100 is a processing platform incorporated within asystem-on-a-chip (SoC) integrated circuit for use in mobile, handheld,or embedded devices such as within Internet-of-things (IoT) devices withwired or wireless connectivity to a local or wide area network.

In one embodiment, system 100 can include, couple with, or be integratedwithin: a server-based gaming platform; a game console, including a gameand media console; a mobile gaming console, a handheld game console, oran online game console. In some embodiments the system 100 is part of amobile phone, smart phone, tablet computing device or mobileInternet-connected device such as a laptop with low internal storagecapacity. Processing system 100 can also include, couple with, or beintegrated within: a wearable device, such as a smart watch wearabledevice; smart eyewear or clothing enhanced with augmented reality (AR)or virtual reality (VR) features to provide visual, audio or tactileoutputs to supplement real world visual, audio or tactile experiences orotherwise provide text, audio, graphics, video, holographic images orvideo, or tactile feedback; other augmented reality (AR) device; orother virtual reality (VR) device. In some embodiments, the processingsystem 100 includes or is part of a television or set top box device. Inone embodiment, system 100 can include, couple with, or be integratedwithin a self-driving vehicle such as a bus, tractor trailer, car, motoror electric power cycle, plane or glider (or any combination thereof).The self-driving vehicle may use system 100 to process the environmentsensed around the vehicle.

In some embodiments, the one or more processors 102 each include one ormore processor cores 107 to process instructions which, when executed,perform operations for system or user software. In some embodiments, atleast one of the one or more processor cores 107 is configured toprocess a specific instruction set 109. In some embodiments, instructionset 109 may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). One or more processor cores 107 may process adifferent instruction set 109, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 107may also include other processing devices, such as a Digital SignalProcessor (DSP).

In some embodiments, the processor 102 includes cache memory 104.Depending on the architecture, the processor 102 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 102. In some embodiments, the processor 102 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 107 using knowncache coherency techniques. A register file 106 can be additionallyincluded in processor 102 and may include different types of registersfor storing different types of data (e.g., integer registers, floatingpoint registers, status registers, and an instruction pointer register).Some registers may be general-purpose registers, while other registersmay be specific to the design of the processor 102.

In some embodiments, one or more processor(s) 102 are coupled with oneor more interface bus(es) 110 to transmit communication signals such asaddress, data, or control signals between processor 102 and othercomponents in the system 100. The interface bus 110, in one embodiment,can be a processor bus, such as a version of the Direct Media Interface(DMI) bus. However, processor busses are not limited to the DMI bus, andmay include one or more Peripheral Component Interconnect buses (e.g.,PCI, PCI express), memory busses, or other types of interface busses. Inone embodiment the processor(s) 102 include an integrated memorycontroller 116 and a platform controller hub 130. The memory controller116 facilitates communication between a memory device and othercomponents of the system 100, while the platform controller hub (PCH)130 provides connections to I/O devices via a local I/O bus.

The memory device 120 can be a dynamic random-access memory (DRAM)device, a static random-access memory (SRAM) device, flash memorydevice, phase-change memory device, or some other memory device havingsuitable performance to serve as process memory. In one embodiment thememory device 120 can operate as system memory for the system 100, tostore data 122 and instructions 121 for use when the one or moreprocessors 102 executes an application or process. Memory controller 116also couples with an optional external graphics processor 118, which maycommunicate with the one or more graphics processors 108 in processors102 to perform graphics and media operations. In some embodiments,graphics, media, and or compute operations may be assisted by anaccelerator 112 which is a coprocessor that can be configured to performa specialized set of graphics, media, or compute operations. Forexample, in one embodiment the accelerator 112 is a matrixmultiplication accelerator used to optimize machine learning or computeoperations. In one embodiment the accelerator 112 is a ray-tracingaccelerator that can be used to perform ray-tracing operations inconcert with the graphics processor 108. In one embodiment, an externalaccelerator 119 may be used in place of or in concert with theaccelerator 112.

In some embodiments a display device 111 can connect to the processor(s)102. The display device 111 can be one or more of an internal displaydevice, as in a mobile electronic device or a laptop device or anexternal display device attached via a display interface (e.g.,DisplayPort, etc.). In one embodiment the display device 111 can be ahead mounted display (HMD) such as a stereoscopic display device for usein virtual reality (VR) applications or augmented reality (AR)applications.

In some embodiments the platform controller hub 130 enables peripheralsto connect to memory device 120 and processor 102 via a high-speed I/Obus. The I/O peripherals include, but are not limited to, an audiocontroller 146, a network controller 134, a firmware interface 128, awireless transceiver 126, touch sensors 125, a data storage device 124(e.g., non-volatile memory, volatile memory, hard disk drive, flashmemory, NAND, 3D NAND, 3D XPoint, etc.). The data storage device 124 canconnect via a storage interface (e.g., SATA) or via a peripheral bus,such as a Peripheral Component Interconnect bus (e.g., PCI, PCIexpress). The touch sensors 125 can include touch screen sensors,pressure sensors, or fingerprint sensors. The wireless transceiver 126can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile networktransceiver such as a 3G, 4G, 5G, or Long-Term Evolution (LTE)transceiver. The firmware interface 128 enables communication withsystem firmware, and can be, for example, a unified extensible firmwareinterface (UEFI). The network controller 134 can enable a networkconnection to a wired network. In some embodiments, a high-performancenetwork controller (not shown) couples with the interface bus 110. Theaudio controller 146, in one embodiment, is a multi-channel highdefinition audio controller. In one embodiment the system 100 includesan optional legacy I/O controller 140 for coupling legacy (e.g.,Personal System 2 (PS/2)) devices to the system. The platform controllerhub 130 can also connect to one or more Universal Serial Bus (USB)controllers 142 connect input devices, such as keyboard and mouse 143combinations, a camera 144, or other USB input devices.

It will be appreciated that the system 100 shown is exemplary and notlimiting, as other types of data processing systems that are differentlyconfigured may also be used. For example, an instance of the memorycontroller 116 and platform controller hub 130 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 118. In one embodiment the platform controller hub 130 and/ormemory controller 116 may be external to the one or more processor(s)102. For example, the system 100 can include an external memorycontroller 116 and platform controller hub 130, which may be configuredas a memory controller hub and peripheral controller hub within a systemchipset that is in communication with the processor(s) 102.

For example, circuit boards (“sleds”) can be used on which componentssuch as CPUs, memory, and other components are placed are designed forincreased thermal performance. In some examples, processing componentssuch as the processors are located on a top side of a sled while nearmemory, such as DIMMs, are located on a bottom side of the sled. As aresult of the enhanced airflow provided by this design, the componentsmay operate at higher frequencies and power levels than in typicalsystems, thereby increasing performance. Furthermore, the sleds areconfigured to blindly mate with power and data communication cables in arack, thereby enhancing their ability to be quickly removed, upgraded,reinstalled, and/or replaced. Similarly, individual components locatedon the sleds, such as processors, accelerators, memory, and data storagedrives, are configured to be easily upgraded due to their increasedspacing from each other. In the illustrative embodiment, the componentsadditionally include hardware attestation features to prove theirauthenticity.

A data center can utilize a single network architecture (“fabric”) thatsupports multiple other network architectures including Ethernet andOmni-Path. The sleds can be coupled to switches via optical fibers,which provide higher bandwidth and lower latency than typical twistedpair cabling (e.g., Category 5, Category 5e, Category 6, etc.). Due tothe high bandwidth, low latency interconnections and networkarchitecture, the data center may, in use, pool resources, such asmemory, accelerators (e.g., GPUs, graphics accelerators, FPGAs, ASICs,neural network and/or artificial intelligence accelerators, etc.), anddata storage drives that are physically disaggregated, and provide themto compute resources (e.g., processors) on an as needed basis, enablingthe compute resources to access the pooled resources as if they werelocal.

A power supply or source can provide voltage and/or current to system100 or any component or system described herein. In one example, thepower supply includes an AC to DC (alternating current to directcurrent) adapter to plug into a wall outlet. Such AC power can berenewable energy (e.g., solar power) power source. In one example, powersource includes a DC power source, such as an external AC to DCconverter. In one example, power source or power supply includeswireless charging hardware to charge via proximity to a charging field.In one example, power source can include an internal battery,alternating current supply, motion-based power supply, solar powersupply, or fuel cell source.

FIGS. 2A-2D illustrate computing systems and graphics processorsprovided by embodiments described herein. The elements of FIGS. 2A-2Dhaving the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such.

FIG. 2A is a block diagram of an embodiment of a processor 200 havingone or more processor cores 202A-202N, an integrated memory controller214, and an integrated graphics processor 208. Processor 200 can includeadditional cores up to and including additional core 202N represented bythe dashed lined boxes. Each of processor cores 202A-202N includes oneor more internal cache units 204A-204N. In some embodiments eachprocessor core also has access to one or more shared cached units 206.The internal cache units 204A-204N and shared cache units 206 representa cache memory hierarchy within the processor 200. The cache memoryhierarchy may include at least one level of instruction and data cachewithin each processor core and one or more levels of shared mid-levelcache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or otherlevels of cache, where the highest level of cache before external memoryis classified as the LLC. In some embodiments, cache coherency logicmaintains coherency between the various cache units 206 and 204A-204N.

In some embodiments, processor 200 may also include a set of one or morebus controller units 216 and a system agent core 210. The one or morebus controller units 216 manage a set of peripheral buses, such as oneor more PCI or PCI express busses. System agent core 210 providesmanagement functionality for the various processor components. In someembodiments, system agent core 210 includes one or more integratedmemory controllers 214 to manage access to various external memorydevices (not shown).

In some embodiments, one or more of the processor cores 202A-202Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 210 includes components for coordinating andoperating cores 202A-202N during multi-threaded processing. System agentcore 210 may additionally include a power control unit (PCU), whichincludes logic and components to regulate the power state of processorcores 202A-202N and graphics processor 208.

In some embodiments, processor 200 additionally includes graphicsprocessor 208 to execute graphics processing operations. In someembodiments, the graphics processor 208 couples with the set of sharedcache units 206, and the system agent core 210, including the one ormore integrated memory controllers 214. In some embodiments, the systemagent core 210 also includes a display controller 211 to drive graphicsprocessor output to one or more coupled displays. In some embodiments,display controller 211 may also be a separate module coupled with thegraphics processor via at least one interconnect, or may be integratedwithin the graphics processor 208.

In some embodiments, a ring-based interconnect unit 212 is used tocouple the internal components of the processor 200. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 208 couples with the ring interconnect 212 via an I/O link213.

The exemplary I/O link 213 represents at least one of multiple varietiesof I/O interconnects, including an on package I/O interconnect whichfacilitates communication between various processor components and ahigh-performance embedded memory module 218, such as an eDRAM module. Insome embodiments, each of the processor cores 202A-202N and graphicsprocessor 208 can use embedded memory modules 218 as a shared Last LevelCache.

In some embodiments, processor cores 202A-202N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 202A-202N are heterogeneous in terms of instruction setarchitecture (ISA), where one or more of processor cores 202A-202Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment, processor cores 202A-202N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. In one embodiment,processor cores 202A-202N are heterogeneous in terms of computationalcapability. Additionally, processor 200 can be implemented on one ormore chips or as an SoC integrated circuit having the illustratedcomponents, in addition to other components.

FIG. 2B is a block diagram of hardware logic of a graphics processorcore 219, according to some embodiments described herein. Elements ofFIG. 2B having the same reference numbers (or names) as the elements ofany other figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Thegraphics processor core 219, sometimes referred to as a core slice, canbe one or multiple graphics cores within a modular graphics processor.The graphics processor core 219 is exemplary of one graphics core slice,and a graphics processor as described herein may include multiplegraphics core slices based on target power and performance envelopes.Each graphics processor core 219 can include a fixed function block 230coupled with multiple sub-cores 221A-221F, also referred to assub-slices, that include modular blocks of general-purpose and fixedfunction logic.

In some embodiments, the fixed function block 230 includes ageometry/fixed function pipeline 231 that can be shared by all sub-coresin the graphics processor core 219, for example, in lower performanceand/or lower power graphics processor implementations. In variousembodiments, the geometry/fixed function pipeline 231 includes a 3Dfixed function pipeline (e.g., 3D pipeline 312 as in FIG. 3 and FIG. 4 ,described below) a video front-end unit, a thread spawner and threaddispatcher, and a unified return buffer manager, which manages unifiedreturn buffers (e.g., unified return buffer 418 in FIG. 4 , as describedbelow).

In one embodiment the fixed function block 230 also includes a graphicsSoC interface 232, a graphics microcontroller 233, and a media pipeline234. The graphics SoC interface 232 provides an interface between thegraphics processor core 219 and other processor cores within a system ona chip integrated circuit. The graphics microcontroller 233 is aprogrammable sub-processor that is configurable to manage variousfunctions of the graphics processor core 219, including thread dispatch,scheduling, and pre-emption. The media pipeline 234 (e.g., mediapipeline 316 of FIG. 3 and FIG. 4 ) includes logic to facilitate thedecoding, encoding, pre-processing, and/or post-processing of multimediadata, including image and video data. The media pipeline 234 implementmedia operations via requests to compute or sampling logic within thesub-cores 221-221F.

In one embodiment the SoC interface 232 enables the graphics processorcore 219 to communicate with general-purpose application processor cores(e.g., CPUs) and/or other components within an SoC, including memoryhierarchy elements such as a shared last level cache memory, the systemRAM, and/or embedded on-chip or on-package DRAM. The SoC interface 232can also enable communication with fixed function devices within theSoC, such as camera imaging pipelines, and enables the use of and/orimplements global memory atomics that may be shared between the graphicsprocessor core 219 and CPUs within the SoC. The SoC interface 232 canalso implement power management controls for the graphics processor core219 and enable an interface between a clock domain of the graphic core219 and other clock domains within the SoC. In one embodiment the SoCinterface 232 enables receipt of command buffers from a command streamerand global thread dispatcher that are configured to provide commands andinstructions to each of one or more graphics cores within a graphicsprocessor. The commands and instructions can be dispatched to the mediapipeline 234, when media operations are to be performed, or a geometryand fixed function pipeline (e.g., geometry and fixed function pipeline231, geometry and fixed function pipeline 237) when graphics processingoperations are to be performed.

The graphics microcontroller 233 can be configured to perform variousscheduling and management tasks for the graphics processor core 219. Inone embodiment the graphics microcontroller 233 can perform graphicsand/or compute workload scheduling on the various graphics parallelengines within execution unit (EU) arrays 222A-222F, 224A-224F withinthe sub-cores 221A-221F. In this scheduling model, host softwareexecuting on a CPU core of an SoC including the graphics processor core219 can submit workloads one of multiple graphic processor doorbells,which invokes a scheduling operation on the appropriate graphics engine.Scheduling operations include determining which workload to run next,submitting a workload to a command streamer, pre-empting existingworkloads running on an engine, monitoring progress of a workload, andnotifying host software when a workload is complete. In one embodimentthe graphics microcontroller 233 can also facilitate low-power or idlestates for the graphics processor core 219, providing the graphicsprocessor core 219 with the ability to save and restore registers withinthe graphics processor core 219 across low-power state transitionsindependently from the operating system and/or graphics driver softwareon the system.

The graphics processor core 219 may have greater than or fewer than theillustrated sub-cores 221A-221F, up to N modular sub-cores. For each setof N sub-cores, the graphics processor core 219 can also include sharedfunction logic 235, shared and/or cache memory 236, a geometry/fixedfunction pipeline 237, as well as additional fixed function logic 238 toaccelerate various graphics and compute processing operations. Theshared function logic 235 can include logic units associated with theshared function logic 420 of FIG. 4 (e.g., sampler, math, and/orinter-thread communication logic) that can be shared by each N sub-coreswithin the graphics processor core 219. The shared and/or cache memory236 can be a last-level cache for the set of N sub-cores 221A-221Fwithin the graphics processor core 219, and can also serve as sharedmemory that is accessible by multiple sub-cores. The geometry/fixedfunction pipeline 237 can be included instead of the geometry/fixedfunction pipeline 231 within the fixed function block 230 and caninclude the same or similar logic units.

In one embodiment the graphics processor core 219 includes additionalfixed function logic 238 that can include various fixed functionacceleration logic for use by the graphics processor core 219. In oneembodiment the additional fixed function logic 238 includes anadditional geometry pipeline for use in position only shading. Inposition-only shading, two geometry pipelines exist, the full geometrypipeline within the geometry/fixed function pipeline 238, 231, and acull pipeline, which is an additional geometry pipeline which may beincluded within the additional fixed function logic 238. In oneembodiment the cull pipeline is a trimmed down version of the fullgeometry pipeline. The full pipeline and the cull pipeline can executedifferent instances of the same application, each instance having aseparate context. Position only shading can hide long cull runs ofdiscarded triangles, enabling shading to be completed earlier in someinstances. For example and in one embodiment the cull pipeline logicwithin the additional fixed function logic 238 can execute positionshaders in parallel with the main application and generally generatescritical results faster than the full pipeline, as the cull pipelinefetches and shades only the position attribute of the vertices, withoutperforming rasterization and rendering of the pixels to the framebuffer. The cull pipeline can use the generated critical results tocompute visibility information for all the triangles without regard towhether those triangles are culled. The full pipeline (which in thisinstance may be referred to as a replay pipeline) can consume thevisibility information to skip the culled triangles to shade only thevisible triangles that are finally passed to the rasterization phase.

In one embodiment the additional fixed function logic 238 can alsoinclude machine-learning acceleration logic, such as fixed functionmatrix multiplication logic, for implementations including optimizationsfor machine learning training or inferencing.

Within each graphics sub-core 221A-221F includes a set of executionresources that may be used to perform graphics, media, and computeoperations in response to requests by graphics pipeline, media pipeline,or shader programs. The graphics sub-cores 221A-221F include multiple EUarrays 222A-222F, 224A-224F, thread dispatch and inter-threadcommunication (TD/IC) logic 223A-223F, a 3D (e.g., texture) sampler225A-225F, a media sampler 206A-206F, a shader processor 227A-227F, andshared local memory (SLM) 228A-228F. The EU arrays 222A-222F, 224A-224Feach include multiple execution units, which are general-purposegraphics processing units capable of performing floating-point andinteger/fixed-point logic operations in service of a graphics, media, orcompute operation, including graphics, media, or compute shaderprograms. The TD/IC logic 223A-223F performs local thread dispatch andthread control operations for the execution units within a sub-core andfacilitate communication between threads executing on the executionunits of the sub-core. The 3D sampler 225A-225F can read texture orother 3D graphics related data into memory. The 3D sampler can readtexture data differently based on a configured sample state and thetexture format associated with a given texture. The media sampler206A-206F can perform similar read operations based on the type andformat associated with media data. In one embodiment, each graphicssub-core 221A-221F can alternately include a unified 3D and mediasampler. Threads executing on the execution units within each of thesub-cores 221A-221F can make use of shared local memory 228A-228F withineach sub-core, to enable threads executing within a thread group toexecute using a common pool of on-chip memory.

FIG. 2C illustrates a graphics processing unit (GPU) 239 that includesdedicated sets of graphics processing resources arranged into multi-coregroups 240A-240N. While the details of only a single multi-core group240A are provided, it will be appreciated that the other multi-coregroups 240B-240N may be equipped with the same or similar sets ofgraphics processing resources.

As illustrated, a multi-core group 240A may include a set of graphicscores 243, a set of tensor cores 244, and a set of ray tracing cores245. A scheduler/dispatcher 241 schedules and dispatches the graphicsthreads for execution on the various cores 243, 244, 245. A set ofregister files 242 store operand values used by the cores 243, 244, 245when executing the graphics threads. These may include, for example,integer registers for storing integer values, floating point registersfor storing floating point values, vector registers for storing packeddata elements (integer and/or floating point data elements) and tileregisters for storing tensor/matrix values. In one embodiment, the tileregisters are implemented as combined sets of vector registers.

One or more combined level 1 (L1) caches and shared memory units 247store graphics data such as texture data, vertex data, pixel data, raydata, bounding volume data, etc., locally within each multi-core group240A. One or more texture units 247 can also be used to performtexturing operations, such as texture mapping and sampling. A Level 2(L2) cache 253 shared by all or a subset of the multi-core groups240A-240N stores graphics data and/or instructions for multipleconcurrent graphics threads. As illustrated, the L2 cache 253 may beshared across a plurality of multi-core groups 240A-240N. One or morememory controllers 248 couple the GPU 239 to a memory 249 which may be asystem memory (e.g., DRAM) and/or a dedicated graphics memory (e.g.,GDDR6 memory).

Input/output (I/O) circuitry 250 couples the GPU 239 to one or more I/Odevices 252 such as digital signal processors (DSPs), networkcontrollers, or user input devices. An on-chip interconnect may be usedto couple the I/O devices 252 to the GPU 239 and memory 249. One or moreI/O memory management units (IOMMUs) 251 of the I/O circuitry 250 couplethe I/O devices 252 directly to the system memory 249. In oneembodiment, the IOMMU 251 manages multiple sets of page tables to mapvirtual addresses to physical addresses in system memory 249. In thisembodiment, the I/O devices 252, CPU(s) 246, and GPU(s) 239 may sharethe same virtual address space.

In one implementation, the IOMMU 251 supports virtualization. In thiscase, it may manage a first set of page tables to map guest/graphicsvirtual addresses to guest/graphics physical addresses and a second setof page tables to map the guest/graphics physical addresses tosystem/host physical addresses (e.g., within system memory 249). Thebase addresses of each of the first and second sets of page tables maybe stored in control registers and swapped out on a context switch(e.g., so that the new context is provided with access to the relevantset of page tables). While not illustrated in FIG. 2C, each of the cores243, 244, 245 and/or multi-core groups 240A-240N may include translationlookaside buffers (TLBs) to cache guest virtual to guest physicaltranslations, guest physical to host physical translations, and guestvirtual to host physical translations.

In one embodiment, the CPUs 246, GPUs 239, and I/O devices 252 areintegrated on a single semiconductor chip and/or chip package. Theillustrated memory 249 may be integrated on the same chip or may becoupled to the memory controllers 248 via an off-chip interface. In oneimplementation, the memory 249 comprises GDDR6 memory which shares thesame virtual address space as other physical system-level memories,although the underlying principles of the invention are not limited tothis specific implementation.

In one embodiment, the tensor cores 244 include a plurality of executionunits specifically designed to perform matrix operations, which are thefundamental compute operation used to perform deep learning operations.For example, simultaneous matrix multiplication operations may be usedfor neural network training and inferencing. The tensor cores 244 mayperform matrix processing using a variety of operand precisionsincluding single precision floating-point (e.g., 32 bits),half-precision floating point (e.g., 16 bits), integer words (16 bits),bytes (8 bits), and half-bytes (4 bits). In one embodiment, a neuralnetwork implementation extracts features of each rendered scene,potentially combining details from multiple frames, to construct ahigh-quality final image.

In deep learning implementations, parallel matrix multiplication workmay be scheduled for execution on the tensor cores 244. The training ofneural networks, in particular, requires a significant number matrix dotproduct operations. In order to process an inner-product formulation ofan N× N×N matrix multiply, the tensor cores 244 may include at least Ndot-product processing elements. Before the matrix multiply begins, oneentire matrix is loaded into tile registers and at least one column of asecond matrix is loaded each cycle for N cycles. Each cycle, there are Ndot products that are processed.

Matrix elements may be stored at different precisions depending on theparticular implementation, including 16-bit words, 8-bit bytes (e.g.,INT8) and 4-bit half-bytes (e.g., INT4). Different precision modes maybe specified for the tensor cores 244 to ensure that the most efficientprecision is used for different workloads (e.g., such as inferencingworkloads which can tolerate quantization to bytes and half-bytes).

In one embodiment, the ray tracing cores 245 accelerate ray tracingoperations for both real-time ray tracing and non-real-time ray tracingimplementations. In particular, the ray tracing cores 245 include raytraversal/intersection circuitry for performing ray traversal usingbounding volume hierarchies (BVHs) and identifying intersections betweenrays and primitives enclosed within the BVH volumes. The ray tracingcores 245 may also include circuitry for performing depth testing andculling (e.g., using a Z buffer or similar arrangement). In oneimplementation, the ray tracing cores 245 perform traversal andintersection operations in concert with the image denoising techniquesdescribed herein, at least a portion of which may be executed on thetensor cores 244. For example, in one embodiment, the tensor cores 244implement a deep learning neural network to perform comprising a localmemory 9010 (and/or system memory) denoising of frames generated by theray tracing cores 245. However, the CPU(s) 246, graphics cores 243,and/or ray tracing cores 245 may also implement all or a portion of thedenoising and/or deep learning algorithms.

In addition, as described above, a distributed approach to denoising maybe employed in which the GPU 239 is in a computing device coupled toother computing devices over a network or high speed interconnect. Inthis embodiment, the interconnected computing devices share neuralnetwork learning/training data to improve the speed with which theoverall system learns to perform denoising for different types of imageframes and/or different graphics applications.

In one embodiment, the ray tracing cores 245 process all BVH traversaland ray-primitive intersections, saving the graphics cores 243 frombeing overloaded with thousands of instructions per ray. In oneembodiment, each ray tracing core 245 includes a first set ofspecialized circuitry for performing bounding box tests (e.g., fortraversal operations) and a second set of specialized circuitry forperforming the ray-triangle intersection tests (e.g., intersecting rayswhich have been traversed). Thus, in one embodiment, the multi-coregroup 240A can simply launch a ray probe, and the ray tracing cores 245independently perform ray traversal and intersection and return hit data(e.g., a hit, no hit, multiple hits, etc.) to the thread context. Theother cores 243, 244 are freed to perform other graphics or compute workwhile the ray tracing cores 245 perform the traversal and intersectionoperations.

In one embodiment, each ray tracing core 245 includes a traversal unitto perform BVH testing operations and an intersection unit whichperforms ray-primitive intersection tests. The intersection unitgenerates a “hit”, “no hit”, or “multiple hit” response, which itprovides to the appropriate thread. During the traversal andintersection operations, the execution resources of the other cores(e.g., graphics cores 243 and tensor cores 244) are freed to performother forms of graphics work.

In one particular embodiment described below, a hybrid rasterization/raytracing approach is used in which work is distributed between thegraphics cores 243 and ray tracing cores 245.

In one embodiment, the ray tracing cores 245 (and/or other cores 243,244) include hardware support for a ray tracing instruction set such asMicrosoft's DirectX Ray Tracing (DXR) which includes a DispatchRayscommand, as well as ray-generation, closest-hit, any-hit, and missshaders, which enable the assignment of unique sets of shaders andtextures for each object. Another ray tracing platform which may besupported by the ray tracing cores 245, graphics cores 243 and tensorcores 244 is Vulkan 1.1.85. Note, however, that the underlyingprinciples of the invention are not limited to any particular raytracing ISA.

In general, the various cores 245, 244, 243 may support a ray tracinginstruction set that includes instructions/functions for ray generation,closest hit, any hit, ray-primitive intersection, per-primitive andhierarchical bounding box construction, miss, visit, and exceptions.More specifically, one embodiment includes ray tracing instructions toperform the following functions:

Ray Generation—Ray generation instructions may be executed for eachpixel, sample, or other user-defined work assignment.

Closest Hit—A closest hit instruction may be executed to locate theclosest intersection point of a ray with primitives within a scene.

Any Hit—An any hit instruction identifies multiple intersections betweena ray and primitives within a scene, potentially to identify a newclosest intersection point.

Intersection—An intersection instruction performs a ray-primitiveintersection test and outputs a result.

Per-primitive Bounding box Construction—This instruction builds abounding box around a given primitive or group of primitives (e.g., whenbuilding a new BVH or other acceleration data structure).

Miss—Indicates that a ray misses all geometry within a scene, orspecified region of a scene.

Visit—Indicates the children volumes a ray will traverse.

Exceptions—Includes various types of exception handlers (e.g., invokedfor various error conditions).

FIG. 2D is a block diagram of general purpose graphics processing unit(GPGPU) 270 that can be configured as a graphics processor and/orcompute accelerator, according to embodiments described herein. TheGPGPU 270 can interconnect with host processors (e.g., one or moreCPU(s) 246) and memory 271, 272 via one or more system and/or memorybusses. In one embodiment the memory 271 is system memory that may beshared with the one or more CPU(s) 246, while memory 272 is devicememory that is dedicated to the GPGPU 270. In one embodiment, componentswithin the GPGPU 270 and device memory 272 may be mapped into memoryaddresses that are accessible to the one or more CPU(s) 246. Access tomemory 271 and 272 may be facilitated via a memory controller 268. Inone embodiment the memory controller 268 includes an internal directmemory access (DMA) controller 269 or can include logic to performoperations that would otherwise be performed by a DMA controller.

The GPGPU 270 includes multiple cache memories, including an L2 cache253, L1 cache 254, an instruction cache 255, and shared memory 256, atleast a portion of which may also be partitioned as a cache memory. TheGPGPU 270 also includes multiple compute units 260A-260N. Each computeunit 260A-260N includes a set of vector registers 261, scalar registers262, vector logic units 263, and scalar logic units 264. The computeunits 260A-260N can also include local shared memory 265 and a programcounter 266. The compute units 260A-260N can couple with a constantcache 267, which can be used to store constant data, which is data thatwill not change during the run of kernel or shader program that executeson the GPGPU 270. In one embodiment the constant cache 267 is a scalardata cache and cached data can be fetched directly into the scalarregisters 262.

During operation, the one or more CPU(s) 246 can write commands intoregisters or memory in the GPGPU 270 that has been mapped into anaccessible address space. The command processors 257 can read thecommands from registers or memory and determine how those commands willbe processed within the GPGPU 270. A thread dispatcher 258 can then beused to dispatch threads to the compute units 260A-260N to perform thosecommands. Each compute unit 260A-260N can execute threads independentlyof the other compute units. Additionally each compute unit 260A-260N canbe independently configured for conditional computation and canconditionally output the results of computation to memory. The commandprocessors 257 can interrupt the one or more CPU(s) 246 when thesubmitted commands are complete.

FIGS. 3A-3C illustrate block diagrams of additional graphics processorand compute accelerator architectures provided by embodiments describedherein. The elements of FIGS. 3A-3C having the same reference numbers(or names) as the elements of any other figure herein can operate orfunction in any manner similar to that described elsewhere herein, butare not limited to such.

FIG. 3A is a block diagram of a graphics processor 300, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores, or other semiconductordevices such as, but not limited to, memory devices or networkinterfaces. In some embodiments, the graphics processor communicates viaa memory mapped I/O interface to registers on the graphics processor andwith commands placed into the processor memory. In some embodiments,graphics processor 300 includes a memory interface 314 to access memory.Memory interface 314 can be an interface to local memory, one or moreinternal caches, one or more shared external caches, and/or to systemmemory.

In some embodiments, graphics processor 300 also includes a displaycontroller 302 to drive display output data to a display device 318.Display controller 302 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. The display device 318 can be an internal orexternal display device. In one embodiment the display device 318 is ahead mounted display device, such as a virtual reality (VR) displaydevice or an augmented reality (AR) display device. In some embodiments,graphics processor 300 includes a video codec engine 306 to encode,decode, or transcode media to, from, or between one or more mediaencoding formats, including, but not limited to Moving Picture ExpertsGroup (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC) formatssuch as H.264/MPEG-4 AVC, H.265/HEVC, Alliance for Open Media (AOMedia)VP8, VP9, as well as the Society of Motion Picture & TelevisionEngineers (SMPTE) 421M/VC-1, and Joint Photographic Experts Group (JPEG)formats such as JPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 300 includes a block imagetransfer (BLIT) engine 304 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 310. In someembodiments, GPE 310 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 310 includes a 3D pipeline 312 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 312 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 315.While 3D pipeline 312 can be used to perform media operations, anembodiment of GPE 310 also includes a media pipeline 316 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 316 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 306. In some embodiments, media pipeline 316 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 315. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 315.

In some embodiments, 3D/Media subsystem 315 includes logic for executingthreads spawned by 3D pipeline 312 and media pipeline 316. In oneembodiment, the pipelines send thread execution requests to 3D/Mediasubsystem 315, which includes thread dispatch logic for arbitrating anddispatching the various requests to available thread executionresources. The execution resources include an array of graphicsexecution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 315 includes one or more internal cachesfor thread instructions and data. In some embodiments, the subsystemalso includes shared memory, including registers and addressable memory,to share data between threads and to store output data.

FIG. 3B illustrates a graphics processor 320 having a tiledarchitecture, according to embodiments described herein. In oneembodiment the graphics processor 320 includes a graphics processingengine cluster 322 having multiple instances of the graphics processingengine 310 of FIG. 3A within a graphics engine tile 310A-310D. Eachgraphics engine tile 310A-310D can be interconnected via a set of tileinterconnects 323A-323F. Each graphics engine tile 310A-310D can also beconnected to a memory module or memory device 326A-326D via memoryinterconnects 325A-325D. The memory devices 326A-326D can use anygraphics memory technology. For example, the memory devices 326A-326Dmay be graphics double data rate (GDDR) memory. The memory devices326A-326D, in one embodiment, are high-bandwidth memory (HBM) modulesthat can be on-die with their respective graphics engine tile 310A-310D.In one embodiment the memory devices 326A-326D are stacked memorydevices that can be stacked on top of their respective graphics enginetile 310A-310D. In one embodiment, each graphics engine tile 310A-310Dand associated memory 326A-326D reside on separate chiplets, which arebonded to a base die or base substrate, as described on further detailin FIGS. 11B-11D.

The graphics processing engine cluster 322 can connect with an on-chipor on-package fabric interconnect 324. The fabric interconnect 324 canenable communication between graphics engine tiles 310A-310D andcomponents such as the video codec 306 and one or more copy engines 304.The copy engines 304 can be used to move data out of, into, and betweenthe memory devices 326A-326D and memory that is external to the graphicsprocessor 320 (e.g., system memory). The fabric interconnect 324 canalso be used to interconnect the graphics engine tiles 310A-310D. Thegraphics processor 320 may optionally include a display controller 302to enable a connection with an external display device 318. The graphicsprocessor may also be configured as a graphics or compute accelerator.In the accelerator configuration, the display controller 302 and displaydevice 318 may be omitted.

The graphics processor 320 can connect to a host system via a hostinterface 328. The host interface 328 can enable communication betweenthe graphics processor 320, system memory, and/or other systemcomponents. The host interface 328 can be, for example a PCI express busor another type of host system interface.

FIG. 3C illustrates a compute accelerator 330, according to embodimentsdescribed herein. The compute accelerator 330 can include architecturalsimilarities with the graphics processor 320 of FIG. 3B and is optimizedfor compute acceleration. A compute engine cluster 332 can include a setof compute engine tiles 340A-340D that include execution logic that isoptimized for parallel or vector-based general-purpose computeoperations. In some embodiments, the compute engine tiles 340A-340D donot include fixed function graphics processing logic, although in oneembodiment one or more of the compute engine tiles 340A-340D can includelogic to perform media acceleration. The compute engine tiles 340A-340Dcan connect to memory 326A-326D via memory interconnects 325A-325D. Thememory 326A-326D and memory interconnects 325A-325D may be similartechnology as in graphics processor 320, or can be different. Thegraphics compute engine tiles 340A-340D can also be interconnected via aset of tile interconnects 323A-323F and may be connected with and/orinterconnected by a fabric interconnect 324. In one embodiment thecompute accelerator 330 includes a large L3 cache 336 that can beconfigured as a device-wide cache. The compute accelerator 330 can alsoconnect to a host processor and memory via a host interface 328 in asimilar manner as the graphics processor 320 of FIG. 3B.

Graphics Processing Engine

FIG. 4 is a block diagram of a graphics processing engine 410 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 410 is a version of theGPE 310 shown in FIG. 3A, and may also represent a graphics engine tile310A-310D of FIG. 3B. Elements of FIG. 4 having the same referencenumbers (or names) as the elements of any other figure herein canoperate or function in any manner similar to that described elsewhereherein, but are not limited to such. For example, the 3D pipeline 312and media pipeline 316 of FIG. 3A are illustrated. The media pipeline316 is optional in some embodiments of the GPE 410 and may not beexplicitly included within the GPE 410. For example and in at least oneembodiment, a separate media and/or image processor is coupled to theGPE 410.

In some embodiments, GPE 410 couples with or includes a command streamer403, which provides a command stream to the 3D pipeline 312 and/or mediapipelines 316. In some embodiments, command streamer 403 is coupled withmemory, which can be system memory, or one or more of internal cachememory and shared cache memory. In some embodiments, command streamer403 receives commands from the memory and sends the commands to 3Dpipeline 312 and/or media pipeline 316. The commands are directivesfetched from a ring buffer, which stores commands for the 3D pipeline312 and media pipeline 316. In one embodiment, the ring buffer canadditionally include batch command buffers storing batches of multiplecommands. The commands for the 3D pipeline 312 can also includereferences to data stored in memory, such as but not limited to vertexand geometry data for the 3D pipeline 312 and/or image data and memoryobjects for the media pipeline 316. The 3D pipeline 312 and mediapipeline 316 process the commands and data by performing operations vialogic within the respective pipelines or by dispatching one or moreexecution threads to a graphics core array 414. In one embodiment thegraphics core array 414 include one or more blocks of graphics cores(e.g., graphics core(s) 415A, graphics core(s) 415B), each blockincluding one or more graphics cores. Each graphics core includes a setof graphics execution resources that includes general-purpose andgraphics specific execution logic to perform graphics and computeoperations, as well as fixed function texture processing and/or machinelearning and artificial intelligence acceleration logic.

In various embodiments the 3D pipeline 312 can include fixed functionand programmable logic to process one or more shader programs, such asvertex shaders, geometry shaders, pixel shaders, fragment shaders,compute shaders, or other shader programs, by processing theinstructions and dispatching execution threads to the graphics corearray 414. The graphics core array 414 provides a unified block ofexecution resources for use in processing these shader programs.Multi-purpose execution logic (e.g., execution units) within thegraphics core(s) 415A-414B of the graphic core array 414 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments, the graphics core array 414 includes executionlogic to perform media functions, such as video and/or image processing.In one embodiment, the execution units include general-purpose logicthat is programmable to perform parallel general-purpose computationaloperations, in addition to graphics processing operations. Thegeneral-purpose logic can perform processing operations in parallel orin conjunction with general-purpose logic within the processor core(s)107 of FIG. 1 or core 202A-202N as in FIG. 2A.

Output data generated by threads executing on the graphics core array414 can output data to memory in a unified return buffer (URB) 418. TheURB 418 can store data for multiple threads. In some embodiments the URB418 may be used to send data between different threads executing on thegraphics core array 414. In some embodiments the URB 418 mayadditionally be used for synchronization between threads on the graphicscore array and fixed function logic within the shared function logic420.

In some embodiments, graphics core array 414 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 410. In one embodiment the execution resourcesare dynamically scalable, such that execution resources may be enabledor disabled as needed.

The graphics core array 414 couples with shared function logic 420 thatincludes multiple resources that are shared between the graphics coresin the graphics core array. The shared functions within the sharedfunction logic 420 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 414. In variousembodiments, shared function logic 420 includes but is not limited tosampler 421, math 422, and inter-thread communication (ITC) 423 logic.Additionally, some embodiments implement one or more cache(s) 425 withinthe shared function logic 420.

A shared function is implemented at least in a case where the demand fora given specialized function is insufficient for inclusion within thegraphics core array 414. Instead a single instantiation of thatspecialized function is implemented as a stand-alone entity in theshared function logic 420 and shared among the execution resourceswithin the graphics core array 414. The precise set of functions thatare shared between the graphics core array 414 and included within thegraphics core array 414 varies across embodiments. In some embodiments,specific shared functions within the shared function logic 420 that areused extensively by the graphics core array 414 may be included withinshared function logic 416 within the graphics core array 414. In variousembodiments, the shared function logic 416 within the graphics corearray 414 can include some or all logic within the shared function logic420. In one embodiment, all logic elements within the shared functionlogic 420 may be duplicated within the shared function logic 416 of thegraphics core array 414. In one embodiment the shared function logic 420is excluded in favor of the shared function logic 416 within thegraphics core array 414.

Execution Units

FIGS. 5A-5C illustrate thread execution logic 500 including an array ofprocessing elements employed in a graphics processor core according toembodiments described herein. Elements of FIGS. 5A-5C having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. FIG. 5A-5B illustrates anoverview of thread execution logic 500, which may be representative ofhardware logic illustrated with each sub-core 221A-221F of FIG. 2B. FIG.5A is representative of an execution unit within a general-purposegraphics processor, while FIG. 5B is representative of an execution unitthat may be used within a compute accelerator.

As illustrated in FIG. 5A, in some embodiments thread execution logic500 includes a shader processor 502, a thread dispatcher 504,instruction cache 506, a scalable execution unit array including aplurality of execution units 508A-508N, a sampler 510, shared localmemory 511, a data cache 512, and a data port 514. In one embodiment thescalable execution unit array can dynamically scale by enabling ordisabling one or more execution units (e.g., any of execution units508A, 508B, 508C, 508D, through 508N-1 and 508N) based on thecomputational requirements of a workload. In one embodiment the includedcomponents are interconnected via an interconnect fabric that links toeach of the components. In some embodiments, thread execution logic 500includes one or more connections to memory, such as system memory orcache memory, through one or more of instruction cache 506, data port514, sampler 510, and execution units 508A-508N. In some embodiments,each execution unit (e.g. 508A) is a stand-alone programmablegeneral-purpose computational unit that is capable of executing multiplesimultaneous hardware threads while processing multiple data elements inparallel for each thread. In various embodiments, the array of executionunits 508A-508N is scalable to include any number individual executionunits.

In some embodiments, the execution units 508A-508N are primarily used toexecute shader programs. A shader processor 502 can process the variousshader programs and dispatch execution threads associated with theshader programs via a thread dispatcher 504. In one embodiment thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units 508A-508N.For example, a geometry pipeline can dispatch vertex, tessellation, orgeometry shaders to the thread execution logic for processing. In someembodiments, thread dispatcher 504 can also process runtime threadspawning requests from the executing shader programs.

In some embodiments, the execution units 508A-508N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 508A-508N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units508A-508N causes a waiting thread to sleep until the requested data hasbeen returned. While the waiting thread is sleeping, hardware resourcesmay be devoted to processing other threads. For example, during a delayassociated with a vertex shader operation, an execution unit can performoperations for a pixel shader, fragment shader, or another type ofshader program, including a different vertex shader. Various embodimentscan apply to use execution by use of Single Instruction Multiple Thread(SIMT) as an alternate to use of SIMD or in addition to use of SIMD.Reference to a SIMD core or operation can apply also to SIMT or apply toSIMD in combination with SIMT.

Each execution unit in execution units 508A-508N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 508A-508N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 54-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

In one embodiment one or more execution units can be combined into afused execution unit 509A-509N having thread control logic (507A-507N)that is common to the fused EUs. Multiple EUs can be fused into an EUgroup. Each EU in the fused EU group can be configured to execute aseparate SIMD hardware thread. The number of EUs in a fused EU group canvary according to embodiments. Additionally, various SIMD widths can beperformed per-EU, including but not limited to SIMD8, SIMD16, andSIMD32. Each fused graphics execution unit 509A-509N includes at leasttwo execution units. For example, fused execution unit 509A includes afirst EU 508A, second EU 508B, and thread control logic 507A that iscommon to the first EU 508A and the second EU 508B. The thread controllogic 507A controls threads executed on the fused graphics executionunit 509A, allowing each EU within the fused execution units 509A-509Nto execute using a common instruction pointer register.

One or more internal instruction caches (e.g., 506) are included in thethread execution logic 500 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,512) are included to cache thread data during thread execution. Threadsexecuting on the execution logic 500 can also store explicitly manageddata in the shared local memory 511. In some embodiments, a sampler 510is included to provide texture sampling for 3D operations and mediasampling for media operations. In some embodiments, sampler 510 includesspecialized texture or media sampling functionality to process textureor media data during the sampling process before providing the sampleddata to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 500 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor502 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 502 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 502dispatches threads to an execution unit (e.g., 508A) via threaddispatcher 504. In some embodiments, shader processor 502 uses texturesampling logic in the sampler 510 to access texture data in texture mapsstored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 514 provides a memory accessmechanism for the thread execution logic 500 to output processed data tomemory for further processing on a graphics processor output pipeline.In some embodiments, the data port 514 includes or couples to one ormore cache memories (e.g., data cache 512) to cache data for memoryaccess via the data port.

In one embodiment, the execution logic 500 can also include a ray tracer505 that can provide ray tracing acceleration functionality. The raytracer 505 can support a ray tracing instruction set that includesinstructions/functions for ray generation. The ray tracing instructionset can be similar to or different from the ray-tracing instruction setsupported by the ray tracing cores 245 in FIG. 2C.

FIG. 5B illustrates exemplary internal details of an execution unit 508,according to embodiments. A graphics execution unit 508 can include aninstruction fetch unit 537, a general register file array (GRF) 524, anarchitectural register file array (ARF) 526, a thread arbiter 522, asend unit 530, a branch unit 532, a set of SIMD floating point units(FPUs) 534, and in one embodiment a set of dedicated integer SIMD ALUs535. The GRF 524 and ARF 526 includes the set of general register filesand architecture register files associated with each simultaneoushardware thread that may be active in the graphics execution unit 508.In one embodiment, per thread architectural state is maintained in theARF 526, while data used during thread execution is stored in the GRF524. The execution state of each thread, including the instructionpointers for each thread, can be held in thread-specific registers inthe ARF 526.

In one embodiment the graphics execution unit 508 has an architecturethat is a combination of Simultaneous Multi-Threading (SMT) andfine-grained Interleaved Multi-Threading (IMT). The architecture has amodular configuration that can be fine-tuned at design time based on atarget number of simultaneous threads and number of registers perexecution unit, where execution unit resources are divided across logicused to execute multiple simultaneous threads. The number of logicalthreads that may be executed by the graphics execution unit 508 is notlimited to the number of hardware threads, and multiple logical threadscan be assigned to each hardware thread.

In one embodiment, the graphics execution unit 508 can co-issue multipleinstructions, which may each be different instructions. The threadarbiter 522 of the graphics execution unit thread 508 can dispatch theinstructions to one of the send unit 530, branch unit 532, or SIMDFPU(s) 534 for execution. Each execution thread can access 128general-purpose registers within the GRF 524, where each register canstore 32 bytes, accessible as a SIMD 8-element vector of 32-bit dataelements. In one embodiment, each execution unit thread has access to 4Kbytes within the GRF 524, although embodiments are not so limited, andgreater or fewer register resources may be provided in otherembodiments. In one embodiment the graphics execution unit 508 ispartitioned into seven hardware threads that can independently performcomputational operations, although the number of threads per executionunit can also vary according to embodiments. For example, in oneembodiment up to 16 hardware threads are supported. In an embodiment inwhich seven threads may access 4 Kbytes, the GRF 524 can store a totalof 28 Kbytes. Where 16 threads may access 4 Kbytes, the GRF 524 canstore a total of 64 Kbytes. Flexible addressing modes can permitregisters to be addressed together to build effectively wider registersor to represent strided rectangular block data structures.

In one embodiment, memory operations, sampler operations, and otherlonger-latency system communications are dispatched via “send”instructions that are executed by the message passing send unit 530. Inone embodiment, branch instructions are dispatched to a dedicated branchunit 532 to facilitate SIMD divergence and eventual convergence.

In one embodiment the graphics execution unit 508 includes one or moreSIMD floating point units (FPU(s)) 534 to perform floating-pointoperations. In one embodiment, the FPU(s) 534 also support integercomputation. In one embodiment the FPU(s) 534 can SIMD execute up to Mnumber of 32-bit floating-point (or integer) operations, or SIMD executeup to 2M 16-bit integer or 16-bit floating-point operations. In oneembodiment, at least one of the FPU(s) provides extended math capabilityto support high-throughput transcendental math functions and doubleprecision 54-bit floating-point. In some embodiments, a set of 8-bitinteger SIMD ALUs 535 are also present, and may be specificallyoptimized to perform operations associated with machine learningcomputations.

In one embodiment, arrays of multiple instances of the graphicsexecution unit 508 can be instantiated in a graphics sub-core grouping(e.g., a sub-slice). For scalability, product architects can choose theexact number of execution units per sub-core grouping. In one embodimentthe execution unit 508 can execute instructions across a plurality ofexecution channels. In a further embodiment, each thread executed on thegraphics execution unit 508 is executed on a different channel.

FIG. 6 illustrates an additional execution unit 600, according to anembodiment. The execution unit 600 may be a compute-optimized executionunit for use in, for example, a compute engine tile 340A-340D as in FIG.3C, but is not limited as such. Variants of the execution unit 600 mayalso be used in a graphics engine tile 310A-310D as in FIG. 3B. In oneembodiment, the execution unit 600 includes a thread control unit 601, athread state unit 602, an instruction fetch/prefetch unit 603, and aninstruction decode unit 604. The execution unit 600 additionallyincludes a register file 606 that stores registers that can be assignedto hardware threads within the execution unit. The execution unit 600additionally includes a send unit 607 and a branch unit 608. In oneembodiment, the send unit 607 and branch unit 608 can operate similarlyas the send unit 530 and a branch unit 532 of the graphics executionunit 508 of FIG. 5B.

The execution unit 600 also includes a compute unit 610 that includesmultiple different types of functional units. In one embodiment thecompute unit 610 includes an ALU unit 611 that includes an array ofarithmetic logic units. The ALU unit 611 can be configured to perform64-bit, 32-bit, and 16-bit integer and floating point operations.Integer and floating point operations may be performed simultaneously.The compute unit 610 can also include a systolic array 612, and a mathunit 613. The systolic array 612 includes a W wide and D deep network ofdata processing units that can be used to perform vector or otherdata-parallel operations in a systolic manner. In one embodiment thesystolic array 612 can be configured to perform matrix operations, suchas matrix dot product operations. In one embodiment the systolic array612 support 16-bit floating point operations, as well as 8-bit and 4-bitinteger operations. In one embodiment the systolic array 612 can beconfigured to accelerate machine learning operations. In suchembodiments, the systolic array 612 can be configured with support forthe bfloat 16-bit floating point format. In one embodiment, a math unit613 can be included to perform a specific subset of mathematicaloperations in an efficient and lower-power manner than then ALU unit611. The math unit 613 can include a variant of math logic that may befound in shared function logic of a graphics processing engine providedby other embodiments (e.g., math logic 422 of the shared function logic420 of FIG. 4 ). In one embodiment the math unit 613 can be configuredto perform 32-bit and 64-bit floating point operations.

The thread control unit 601 includes logic to control the execution ofthreads within the execution unit. The thread control unit 601 caninclude thread arbitration logic to start, stop, and preempt executionof threads within the execution unit 600. The thread state unit 602 canbe used to store thread state for threads assigned to execute on theexecution unit 600. Storing the thread state within the execution unit600 enables the rapid pre-emption of threads when those threads becomeblocked or idle. The instruction fetch/prefetch unit 603 can fetchinstructions from an instruction cache of higher level execution logic(e.g., instruction cache 506 as in FIG. 5A). The instructionfetch/prefetch unit 603 can also issue prefetch requests forinstructions to be loaded into the instruction cache based on ananalysis of currently executing threads. The instruction decode unit 604can be used to decode instructions to be executed by the compute units.In one embodiment, the instruction decode unit 604 can be used as asecondary decoder to decode complex instructions into constituentmicrooperations.

The execution unit 600 additionally includes a register file 606 thatcan be used by hardware threads executing on the execution unit 600.Registers in the register file 606 can be divided across the logic usedto execute multiple simultaneous threads within the compute unit 610 ofthe execution unit 600. The number of logical threads that may beexecuted by the graphics execution unit 600 is not limited to the numberof hardware threads, and multiple logical threads can be assigned toeach hardware thread. The size of the register file 606 can vary acrossembodiments based on the number of supported hardware threads. In oneembodiment, register renaming may be used to dynamically allocateregisters to hardware threads.

FIG. 7 is a block diagram illustrating a graphics processor instructionformats 700 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 700 described and illustrated are macro-instructions,in that they are instructions supplied to the execution unit, as opposedto micro-operations resulting from instruction decode once theinstruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 710. A 64-bitcompacted instruction format 730 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 710 provides access toall instruction options, while some options and operations arerestricted in the 64-bit format 730. The native instructions availablein the 64-bit format 730 vary by embodiment. In some embodiments, theinstruction is compacted in part using a set of index values in an indexfield 713. The execution unit hardware references a set of compactiontables based on the index values and uses the compaction table outputsto reconstruct a native instruction in the 128-bit instruction format710. Other sizes and formats of instruction can be used.

For each format, instruction opcode 712 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 714 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 710 an exec-size field716 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 716 is not available foruse in the 64-bit compact instruction format 730.

Some execution unit instructions have up to three operands including twosource operands, src0 720, src1 722, and one destination 718. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 724), where the instructionopcode 712 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726, which specifies an address mode and/or anaccess mode for the instruction. In one embodiment the access mode isused to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 726 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 712bit-fields to simplify Opcode decode 740. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 742 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 742 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 744 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 746 includes amix of instructions, including synchronization instructions (e.g., wait,send) in the form of 0011xxxxb (e.g., 0x30). A parallel math instructiongroup 748 includes component-wise arithmetic instructions (e.g., add,multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). The parallel mathgroup 748 performs the arithmetic operations in parallel across datachannels. The vector math group 750 includes arithmetic instructions(e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). The vector math groupperforms arithmetic such as dot product calculations on vector operands.The illustrated opcode decode 740, in one embodiment, can be used todetermine which portion of an execution unit will be used to execute adecoded instruction. For example, some instructions may be designated assystolic instructions that will be performed by a systolic array. Otherinstructions, such as ray-tracing instructions (not shown) can be routedto a ray-tracing core or ray-tracing logic within a slice or partitionof execution logic.

Graphics Pipeline

FIG. 8 is a block diagram of another embodiment of a graphics processor800. Elements of FIG. 8 having the same reference numbers (or names) asthe elements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, graphics processor 800 includes a geometry pipeline820, a media pipeline 830, a display engine 840, thread execution logic850, and a render output pipeline 870. In some embodiments, graphicsprocessor 800 is a graphics processor within a multi-core processingsystem that includes one or more general-purpose processing cores. Thegraphics processor is controlled by register writes to one or morecontrol registers (not shown) or via commands issued to graphicsprocessor 800 via a ring interconnect 802. In some embodiments, ringinterconnect 802 couples graphics processor 800 to other processingcomponents, such as other graphics processors or general-purposeprocessors. Commands from ring interconnect 802 are interpreted by acommand streamer 803, which supplies instructions to individualcomponents of the geometry pipeline 820 or the media pipeline 830.

In some embodiments, command streamer 803 directs the operation of avertex fetcher 805 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 803. In someembodiments, vertex fetcher 805 provides vertex data to a vertex shader807, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 805 andvertex shader 807 execute vertex-processing instructions by dispatchingexecution threads to execution units 852A-852B via a thread dispatcher831.

In some embodiments, execution units 852A-852B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 852A-852B have anattached L1 cache 851 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, geometry pipeline 820 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 813 operatesat the direction of hull shader 811 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to geometry pipeline 820. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 811, tessellator 813, and domain shader 817) can bebypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 819 via one or more threads dispatched to executionunits 852A-852B, or can proceed directly to the clipper 829. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader819 receives input from the vertex shader 807. In some embodiments,geometry shader 819 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 829 processes vertex data. The clipper829 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 873 in the render output pipeline870 dispatches pixel shaders to convert the geometric objects into perpixel representations. In some embodiments, pixel shader logic isincluded in thread execution logic 850. In some embodiments, anapplication can bypass the rasterizer and depth test component 873 andaccess un-rasterized vertex data via a stream out unit 823.

The graphics processor 800 has an interconnect bus, interconnect fabric,or some other interconnect mechanism that allows data and messagepassing amongst the major components of the processor. In someembodiments, execution units 852A-852B and associated logic units (e.g.,L1 cache 851, sampler 854, texture cache 858, etc.) interconnect via adata port 856 to perform memory access and communicate with renderoutput pipeline components of the processor. In some embodiments,sampler 854, caches 851, 858 and execution units 852A-852B each haveseparate memory access paths. In one embodiment the texture cache 858can also be configured as a sampler cache.

In some embodiments, render output pipeline 870 contains a rasterizerand depth test component 873 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache 878and depth cache 879 are also available in some embodiments. A pixeloperations component 877 performs pixel-based operations on the data,though in some instances, pixel operations associated with 2D operations(e.g. bit block image transfers with blending) are performed by the 2Dengine 841, or substituted at display time by the display controller 843using overlay display planes. In some embodiments, a shared L3 cache 875is available to all graphics components, allowing the sharing of datawithout the use of main system memory.

In some embodiments, graphics processor media pipeline 830 includes amedia engine 837 and a video front-end 834. In some embodiments, videofront-end 834 receives pipeline commands from the command streamer 803.In some embodiments, media pipeline 830 includes a separate commandstreamer. In some embodiments, video front-end 834 processes mediacommands before sending the command to the media engine 837. In someembodiments, media engine 837 includes thread spawning functionality tospawn threads for dispatch to thread execution logic 850 via threaddispatcher 831.

In some embodiments, graphics processor 800 includes a display engine840. In some embodiments, display engine 840 is external to processor800 and couples with the graphics processor via the ring interconnect802, or some other interconnect bus or fabric. In some embodiments,display engine 840 includes a 2D engine 841 and a display controller843. In some embodiments, display engine 840 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 843 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, the geometry pipeline 820 and media pipeline 830are configurable to perform operations based on multiple graphics andmedia programming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 9A is a block diagram illustrating a graphics processor commandformat 900 according to some embodiments. FIG. 9B is a block diagramillustrating a graphics processor command sequence 910 according to anembodiment. The solid lined boxes in FIG. 9A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 900 of FIG. 9A includes data fields to identify a client902, a command operation code (opcode) 904, and data 906 for thecommand. A sub-opcode 905 and a command size 908 are also included insome commands.

In some embodiments, client 902 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 904 and, if present, sub-opcode 905 to determine theoperation to perform. The client unit performs the command usinginformation in data field 906. For some commands an explicit commandsize 908 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word. Othercommand formats can be used.

The flow diagram in FIG. 9B illustrates an exemplary graphics processorcommand sequence 910. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 910 maybegin with a pipeline flush command 912 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 922 and the media pipeline 924 do notoperate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 912 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 913 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 913 isrequired only once within an execution context before issuing pipelinecommands unless the context is to issue commands for both pipelines. Insome embodiments, a pipeline flush command 912 is required immediatelybefore a pipeline switch via the pipeline select command 913.

In some embodiments, a pipeline control command 914 configures agraphics pipeline for operation and is used to program the 3D pipeline922 and the media pipeline 924. In some embodiments, pipeline controlcommand 914 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 914 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 916 are used toconfigure a set of return buffers for the respective pipelines to writedata. Some pipeline operations require the allocation, selection, orconfiguration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments, thereturn buffer state 916 includes selecting the size and number of returnbuffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 920,the command sequence is tailored to the 3D pipeline 922 beginning withthe 3D pipeline state 930 or the media pipeline 924 beginning at themedia pipeline state 940.

The commands to configure the 3D pipeline state 930 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 930 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 932 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 932 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 932command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 932 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 922 dispatches shader execution threads to graphicsprocessor execution units.

In some embodiments, 3D pipeline 922 is triggered via an execute 934command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 910 followsthe media pipeline 924 path when performing media operations. Ingeneral, the specific use and manner of programming for the mediapipeline 924 depends on the media or compute operations to be performed.Specific media decode operations may be offloaded to the media pipelineduring media decode. In some embodiments, the media pipeline can also bebypassed and media decode can be performed in whole or in part usingresources provided by one or more general-purpose processing cores. Inone embodiment, the media pipeline also includes elements forgeneral-purpose graphics processor unit (GPGPU) operations, where thegraphics processor is used to perform SIMD vector operations usingcomputational shader programs that are not explicitly related to therendering of graphics primitives.

In some embodiments, media pipeline 924 is configured in a similarmanner as the 3D pipeline 922. A set of commands to configure the mediapipeline state 940 are dispatched or placed into a command queue beforethe media object commands 942. In some embodiments, commands for themedia pipeline state 940 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,commands for the media pipeline state 940 also support the use of one ormore pointers to “indirect” state elements that contain a batch of statesettings.

In some embodiments, media object commands 942 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 942. Once the pipeline state is configured andmedia object commands 942 are queued, the media pipeline 924 istriggered via an execute command 944 or an equivalent execute event(e.g., register write). Output from media pipeline 924 may then be postprocessed by operations provided by the 3D pipeline 922 or the mediapipeline 924. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 10 illustrates an exemplary graphics software architecture for adata processing system 1000 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application1010, an operating system 1020, and at least one processor 1030. In someembodiments, processor 1030 includes a graphics processor 1032 and oneor more general-purpose processor core(s) 1034. The graphics application1010 and operating system 1020 each execute in the system memory 1050 ofthe data processing system.

In some embodiments, 3D graphics application 1010 contains one or moreshader programs including shader instructions 1012. The shader languageinstructions may be in a high-level shader language, such as theHigh-Level Shader Language (HLSL) of Direct3D, the OpenGL ShaderLanguage (GLSL), and so forth. The application also includes executableinstructions 1014 in a machine language suitable for execution by thegeneral-purpose processor core 1034. The application also includesgraphics objects 1016 defined by vertex data.

In some embodiments, operating system 1020 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 1020 can support agraphics API 1022 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 1020uses a front-end shader compiler 1024 to compile any shader instructions1012 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 1010. In some embodiments, the shader instructions 1012 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 1026 contains a back-endshader compiler xxxx to convert the shader instructions 1012 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 1012 in the GLSL high-level language are passed to a usermode graphics driver 1026 for compilation. In some embodiments, usermode graphics driver 1026 uses operating system kernel mode functions1028 to communicate with a kernel mode graphics driver 1029. In someembodiments, kernel mode graphics driver 1029 communicates with graphicsprocessor 1032 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 11A is a block diagram illustrating an IP core development system1100 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system1100 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility1130 can generate a software simulation 1110 of an IP core design in ahigh-level programming language (e.g., C/C++). The software simulation1110 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 1112. The simulation model 1112 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 1115 can then be created or synthesized from thesimulation model 1112. The RTL design 1115 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 1115, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 1115 or equivalent may be further synthesized by thedesign facility into a hardware model 1120, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3rdparty fabrication facility 1165 using non-volatile memory 1140 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 1150 or wireless connection 1160. Thefabrication facility 1165 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

FIG. 11B illustrates a cross-section side view of an integrated circuitpackage assembly 1170, according to some embodiments described herein.The integrated circuit package assembly 1170 illustrates animplementation of one or more processor or accelerator devices asdescribed herein. The package assembly 1170 includes multiple units ofhardware logic 1172, 1174 connected to a substrate 1180. The logic 1172,1174 may be implemented at least partly in configurable logic orfixed-functionality logic hardware, and can include one or more portionsof any of the processor core(s), graphics processor(s), or otheraccelerator devices described herein. Each unit of logic 1172, 1174 canbe implemented within a semiconductor die and coupled with the substrate1180 via an interconnect structure 1173. The interconnect structure 1173may be configured to route electrical signals between the logic 1172,1174 and the substrate 1180, and can include interconnects such as, butnot limited to bumps or pillars. In some embodiments, the interconnectstructure 1173 may be configured to route electrical signals such as,for example, input/output (I/O) signals and/or power or ground signalsassociated with the operation of the logic 1172, 1174. In someembodiments, the substrate 1180 is an epoxy-based laminate substrate.The substrate 1180 may include other suitable types of substrates inother embodiments. The package assembly 1170 can be connected to otherelectrical devices via a package interconnect 1183. The packageinterconnect 1183 may be coupled to a surface of the substrate 1180 toroute electrical signals to other electrical devices, such as amotherboard, other chipset, or multi-chip module.

In some embodiments, the units of logic 1172, 1174 are electricallycoupled with a bridge 1182 that is configured to route electricalsignals between the logic 1172, 1174. The bridge 1182 may be a denseinterconnect structure that provides a route for electrical signals. Thebridge 1182 may include a bridge substrate composed of glass or asuitable semiconductor material. Electrical routing features can beformed on the bridge substrate to provide a chip-to-chip connectionbetween the logic 1172, 1174.

Although two units of logic 1172, 1174 and a bridge 1182 areillustrated, embodiments described herein may include more or fewerlogic units on one or more dies. The one or more dies may be connectedby zero or more bridges, as the bridge 1182 may be excluded when thelogic is included on a single die. Alternatively, multiple dies or unitsof logic can be connected by one or more bridges. Additionally, multiplelogic units, dies, and bridges can be connected together in otherpossible configurations, including three-dimensional configurations.

FIG. 11C illustrates a package assembly 1190 that includes multipleunits of hardware logic chiplets connected to a substrate 1180 (e.g.,base die). A graphics processing unit, parallel processor, and/orcompute accelerator as described herein can be composed from diversesilicon chiplets that are separately manufactured. In this context, achiplet is an at least partially packaged integrated circuit thatincludes distinct units of logic that can be assembled with otherchiplets into a larger package. A diverse set of chiplets with differentIP core logic can be assembled into a single device. Additionally thechiplets can be integrated into a base die or base chiplet using activeinterposer technology. The concepts described herein enable theinterconnection and communication between the different forms of IPwithin the GPU. IP cores can be manufactured using different processtechnologies and composed during manufacturing, which avoids thecomplexity of converging multiple IPs, especially on a large SoC withseveral flavors IPs, to the same manufacturing process. Enabling the useof multiple process technologies improves the time to market andprovides a cost-effective way to create multiple product SKUs.Additionally, the disaggregated IPs are more amenable to being powergated independently, components that are not in use on a given workloadcan be powered off, reducing overall power consumption.

The hardware logic chiplets can include special purpose hardware logicchiplets 1172, logic or I/O chiplets 1174, and/or memory chiplets 1175.The hardware logic chiplets 1172 and logic or I/O chiplets 1174 may beimplemented at least partly in configurable logic or fixed-functionalitylogic hardware and can include one or more portions of any of theprocessor core(s), graphics processor(s), parallel processors, or otheraccelerator devices described herein. The memory chiplets 1175 can beDRAM (e.g., GDDR, HBM) memory or cache (SRAM) memory.

Each chiplet can be fabricated as separate semiconductor die and coupledwith the substrate 1180 via an interconnect structure 1173. Theinterconnect structure 1173 may be configured to route electricalsignals between the various chiplets and logic within the substrate1180. The interconnect structure 1173 can include interconnects such as,but not limited to bumps or pillars. In some embodiments, theinterconnect structure 1173 may be configured to route electricalsignals such as, for example, input/output (I/O) signals and/or power orground signals associated with the operation of the logic, I/O andmemory chiplets.

In some embodiments, the substrate 1180 is an epoxy-based laminatesubstrate. The substrate 1180 may include other suitable types ofsubstrates in other embodiments. The package assembly 1190 can beconnected to other electrical devices via a package interconnect 1183.The package interconnect 1183 may be coupled to a surface of thesubstrate 1180 to route electrical signals to other electrical devices,such as a motherboard, other chipset, or multi-chip module.

In some embodiments, a logic or I/O chiplet 1174 and a memory chiplet1175 can be electrically coupled via a bridge 1187 that is configured toroute electrical signals between the logic or I/O chiplet 1174 and amemory chiplet 1175. The bridge 1187 may be a dense interconnectstructure that provides a route for electrical signals. The bridge 1187may include a bridge substrate composed of glass or a suitablesemiconductor material. Electrical routing features can be formed on thebridge substrate to provide a chip-to-chip connection between the logicor I/O chiplet 1174 and a memory chiplet 1175. The bridge 1187 may alsobe referred to as a silicon bridge or an interconnect bridge. Forexample, the bridge 1187, in some embodiments, is an Embedded Multi-dieInterconnect Bridge (EMIB). In some embodiments, the bridge 1187 maysimply be a direct connection from one chiplet to another chiplet.

The substrate 1180 can include hardware components for I/O 1191, cachememory 1192, and other hardware logic 1193. A fabric 1185 can beembedded in the substrate 1180 to enable communication between thevarious logic chiplets and the logic 1191, 1193 within the substrate1180. In one embodiment, the I/O 1191, fabric 1185, cache, bridge, andother hardware logic 1193 can be integrated into a base die that islayered on top of the substrate 1180.

In various embodiments a package assembly 1190 can include fewer orgreater number of components and chiplets that are interconnected by afabric 1185 or one or more bridges 1187. The chiplets within the packageassembly 1190 may be arranged in a 3D or 2.5D arrangement. In general,bridge structures 1187 may be used to facilitate a point to pointinterconnect between, for example, logic or I/O chiplets and memorychiplets. The fabric 1185 can be used to interconnect the various logicand/or I/O chiplets (e.g., chiplets 1172, 1174, 1191, 1193). with otherlogic and/or I/O chiplets. In one embodiment, the cache memory 1192within the substrate can act as a global cache for the package assembly1190, part of a distributed global cache, or as a dedicated cache forthe fabric 1185.

FIG. 11D illustrates a package assembly 1194 including interchangeablechiplets 1195, according to an embodiment. The interchangeable chiplets1195 can be assembled into standardized slots on one or more basechiplets 1196, 1198. The base chiplets 1196, 1198 can be coupled via abridge interconnect 1197, which can be similar to the other bridgeinterconnects described herein and may be, for example, an EMIB. Memorychiplets can also be connected to logic or I/O chiplets via a bridgeinterconnect. I/O and logic chiplets can communicate via an interconnectfabric. The base chiplets can each support one or more slots in astandardized format for one of logic or I/O or memory/cache.

In one embodiment, SRAM and power delivery circuits can be fabricatedinto one or more of the base chiplets 1196, 1198, which can befabricated using a different process technology relative to theinterchangeable chiplets 1195 that are stacked on top of the basechiplets. For example, the base chiplets 1196, 1198 can be fabricatedusing a larger process technology, while the interchangeable chipletscan be manufactured using a smaller process technology. One or more ofthe interchangeable chiplets 1195 may be memory (e.g., DRAM) chiplets.Different memory densities can be selected for the package assembly 1194based on the power, and/or performance targeted for the product thatuses the package assembly 1194. Additionally, logic chiplets with adifferent number of type of functional units can be selected at time ofassembly based on the power, and/or performance targeted for theproduct. Additionally, chiplets containing IP logic cores of differingtypes can be inserted into the interchangeable chiplet slots, enablinghybrid processor designs that can mix and match different technology IPblocks.

Exemplary System on a Chip Integrated Circuit

FIGS. 12-13 illustrate exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general-purpose processor cores.

FIG. 12 is a block diagram illustrating an exemplary system on a chipintegrated circuit 1200 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 1200includes one or more application processor(s) 1205 (e.g., CPUs), atleast one graphics processor 1210, and may additionally include an imageprocessor 1215 and/or a video processor 1220, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 1200 includes peripheral or bus logic including a USBcontroller 1225, UART controller 1230, an SPI/SDIO controller 1235, andan I2S/I2C controller 1240. Additionally, the integrated circuit caninclude a display device 1245 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 1250 and a mobileindustry processor interface (MIPI) display interface 1255. Storage maybe provided by a flash memory subsystem 1260 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 1265 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine1270.

FIGS. 13-14 are block diagrams illustrating exemplary graphicsprocessors for use within an SoC, according to embodiments describedherein. FIG. 13 illustrates an exemplary graphics processor 1310 of asystem on a chip integrated circuit that may be fabricated using one ormore IP cores, according to an embodiment. FIG. 13B illustrates anadditional exemplary graphics processor 1340 of a system on a chipintegrated circuit that may be fabricated using one or more IP cores,according to an embodiment. Graphics processor 1310 of FIG. 13 is anexample of a low power graphics processor core. Graphics processor 1340of FIG. 13B is an example of a higher performance graphics processorcore. Each of the graphics processors 1310, 1340 can be variants of thegraphics processor 1210 of FIG. 12 .

As shown in FIG. 13 , graphics processor 1310 includes a vertexprocessor 1305 and one or more fragment processor(s) 1315A-1315N (e.g.,1315A, 1315B, 1315C, 1315D, through 1315N-1, and 1315N). Graphicsprocessor 1310 can execute different shader programs via separate logic,such that the vertex processor 1305 is optimized to execute operationsfor vertex shader programs, while the one or more fragment processor(s)1315A-1315N execute fragment (e.g., pixel) shading operations forfragment or pixel shader programs. The vertex processor 1305 performsthe vertex processing stage of the 3D graphics pipeline and generatesprimitives and vertex data. The fragment processor(s) 1315A-1315N usethe primitive and vertex data generated by the vertex processor 1305 toproduce a framebuffer that is displayed on a display device. In oneembodiment, the fragment processor(s) 1315A-1315N are optimized toexecute fragment shader programs as provided for in the OpenGL API,which may be used to perform similar operations as a pixel shaderprogram as provided for in the Direct 3D API.

Graphics processor 1310 additionally includes one or more memorymanagement units (MMUs) 1320A-1320B, cache(s) 1325A-1325B, and circuitinterconnect(s) 1330A-1330B. The one or more MMU(s) 1320A-1320B providefor virtual to physical address mapping for the graphics processor 1310,including for the vertex processor 1305 and/or fragment processor(s)1315A-1315N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 1325A-1325B. In one embodiment the one or more MMU(s)1320A-1320B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 1205, image processor 1215, and/or video processor 1220 ofFIG. 12 , such that each processor 1205-1220 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 1330A-1330B enable graphics processor 1310 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

As shown FIG. 14 , graphics processor 1340 includes the one or moreMMU(s) 1320A-1320B, cache(s) 1325A-1325B, and circuit interconnect(s)1330A-1330B of the graphics processor 1310 of FIG. 13A. Graphicsprocessor 1340 includes one or more shader core(s) 1355A-1355N (e.g.,1455A, 1355B, 1355C, 1355D, 1355E, 1355F, through 1355N-1, and 1355N),which provides for a unified shader core architecture in which a singlecore or type or core can execute all types of programmable shader code,including shader program code to implement vertex shaders, fragmentshaders, and/or compute shaders. The exact number of shader corespresent can vary among embodiments and implementations. Additionally,graphics processor 1340 includes an inter-core task manager 1345, whichacts as a thread dispatcher to dispatch execution threads to one or moreshader cores 1355A-1355N and a tiling unit 1358 to accelerate tilingoperations for tile-based rendering, in which rendering operations for ascene are subdivided in image space, for example to exploit localspatial coherence within a scene or to optimize use of internal caches.

Ray Tracing with Machine Learning

As mentioned above, ray tracing is a graphics processing technique inwhich a light transport is simulated through physically-based rendering.One of the key operations in ray tracing is processing a visibilityquery which requires traversal and intersection testing of nodes in abounding volume hierarchy (BVH).

Ray- and path-tracing based techniques compute images by tracing raysand paths through each pixel, and using random sampling to computeadvanced effects such as shadows, glossiness, indirect illumination,etc. Using only a few samples is fast but produces noisy images whileusing many samples produces high quality images, but is costprohibitive.

Machine learning includes any circuitry, program code, or combinationthereof capable of progressively improving performance of a specifiedtask or rendering progressively more accurate predictions or decisions.Some machine learning engines can perform these tasks or render thesepredictions/decisions without being explicitly programmed to perform thetasks or render the predictions/decisions. A variety of machine learningtechniques exist including (but not limited to) supervised andsemi-supervised learning, unsupervised learning, and reinforcementlearning.

In the last several years, a breakthrough solution to ray-/path-tracingfor real-time use has come in the form of “denoising”—the process ofusing image processing techniques to produce high quality,filtered/denoised images from noisy, low-sample count inputs. The mosteffective denoising techniques rely on machine learning techniques wherea machine-learning engine learns what a noisy image would likely looklike if it had been computed with more samples. In one particularimplementation, the machine learning is performed by a convolutionalneural network (CNN); however, the underlying principles of theinvention are not limited to a CNN implementation. In such animplementation, training data is produced with low-sample count inputsand ground-truth. The CNN is trained to predict the converged pixel froma neighborhood of noisy pixel inputs around the pixel in question.

Though not perfect, this AI-based denoising technique has provensurprisingly effective. The caveat, however, is that good training datais required, since the network may otherwise predict the wrong results.For example, if an animated movie studio trained a denoising CNN on pastmovies with scenes on land and then attempted to use the trained CNN todenoise frames from a new movie set on water, the denoising operationwill perform sub-optimally.

To address this problem, learning data can be dynamically gathered,while rendering, and a machine learning engine, such as a CNN, may becontinuously trained based on the data on which it is currently beingrun, thus continuously improving the machine learning engine for thetask at hand. Therefore, a training phase may still performed prior toruntime, but continued to adjust the machine learning weights as neededduring runtime. Thereby, the high cost of computing the reference datarequired for the training is avoided by restricting the generation oflearning data to a sub-region of the image every frame or every Nframes. In particular, the noisy inputs of a frame are generated fordenoising the full frame with the current network. In addition, a smallregion of reference pixels are generated and used for continuoustraining, as described below.

While a CNN implementation is described herein, any form of machinelearning engine may be used including, but not limited to systems whichperform supervised learning (e.g., building a mathematical model of aset of data that contains both the inputs and the desired outputs),unsupervised learning (e.g., which evaluate the input data for certaintypes of structure), and/or a combination of supervised and unsupervisedlearning.

Existing de-noising implementations operate in a training phase and aruntime phase. During the training phase, a network topology is definedwhich receives a region of N×N pixels with various per-pixel datachannels such as pixel color, depth, normal, normal deviation, primitiveIDs, and albedo and generates a final pixel color. A set of“representative” training data is generated using one frame's worth oflow-sample count inputs, and referencing the “desired” pixel colorscomputed with a very high sample count. The network is trained towardsthese inputs, generating a set of “ideal” weights for the network. Inthese implementations, the reference data is used to train the network'sweights to most closely match the network's output to the desiredresult.

At runtime, the given, pre-computed ideal network weights are loaded andthe network is initialized. For each frame, a low-sample count image ofdenoising inputs (i.e., the same as used for training) is generated. Foreach pixel, the given neighborhood of pixels' inputs is run through thenetwork to predict the “denoised” pixel color, generating a denoisedframe.

FIG. 15 illustrates an initial training implementation. A machinelearning engine 1500 (e.g., a CNN) receives a region of N×N pixels ashigh sample count image data 1702 with various per-pixel data channelssuch as pixel color, depth, normal, normal deviation, primitive IDs, andalbedo and generates final pixel colors. Representative training data isgenerated using one frame's worth of low-sample count inputs 1501. Thenetwork is trained towards these inputs, generating a set of “ideal”weights 1505 which the machine learning engine 1500 subsequently uses todenoise low sample count images at runtime.

To improve the above techniques, the denoising phase to generate newtraining data every frame or a subset of frames (e.g., every N frameswhere N=2, 3, 4, 10, 25, etc) is augmented. In particular, asillustrated in FIG. 16 , one or more regions in each frame are chosen,referred to here as “new reference regions” 1602 which are rendered witha high sample count into a separate high sample count buffer 1604. A lowsample count buffer 1603 stores the low sample count input frame 1601(including the low sample region 1604 corresponding to the new referenceregion 1602).

The location of the new reference region 1602 may be randomly selected.Alternatively, the location of the new reference region 1602 may beadjusted in a pre-specified manner for each new frame (e.g., using apredefined movement of the region between frames, limited to a specifiedregion in the center of the frame, etc).

Regardless of how the new reference region is selected, it is used bythe machine learning engine 1600 to continually refine and update thetrained weights 1605 used for denoising. In particular, reference pixelcolors from each new reference region 1602 and noisy reference pixelinputs from a corresponding low sample count region 1607 are rendered.Supplemental training is then performed on the machine learning engine1600 using the high-sample-count reference region 1602 and thecorresponding low sample count region 1607. In contrast to the initialtraining, this training is performed continuously during runtime foreach new reference region 1602—thereby ensuring that the machinelearning engine 1600 is precisely trained. For example, per-pixel datachannels (e.g., pixel color, depth, normal, normal deviation, etc) maybe evaluated, which the machine learning engine 1600 uses to makeadjustments to the trained weights 1605. As in the training case (FIG.15 ), the machine learning engine 1600 is trained towards a set of idealweights 1605 for removing noise from the low sample count input frame1601 to generate the denoised frame 1620. However, the trained weights1605 are continually updated, based on new image characteristics of newtypes of low sample count input frames 1601.

The re-training operations performed by the machine learning engine 1600may be executed concurrently in a background process on the graphicsprocessor unit (GPU) or host processor. The render loop, which may beimplemented as a driver component and/or a GPU hardware component, maycontinuously produce new training data (e.g., in the form of newreference regions 1602) which it places in a queue. The backgroundtraining process, executed on the GPU or host processor, maycontinuously read the new training data from this queue, re-trains themachine learning engine 1600, and update it with new weights 1605 atappropriate intervals.

FIG. 17 illustrates an example of one such implementation in which thebackground training process 1700 is implemented by the host CPU 1710. Inparticular, the background training process 1700 uses the high samplecount new reference region 1602 and the corresponding low sample region1604 to continually update the trained weights 1605, thereby updatingthe machine learning engine 1600.

As illustrated in FIG. 18A for the non-limiting example of amulti-player online game, different host machines 1820-1822 individuallygenerate reference regions which a background training process 1700A-Ctransmits to a server 1800 (e.g., such as a gaming server). The server1800 then performs training on a machine learning engine 1810 using thenew reference regions received from each of the hosts 1821-1822,updating the weights 1805 as previously described. It transmits theseweights 1805 to the host machines 1820 which store the weights 1605A-C,thereby updating each individual machine learning engine (not shown).Because the server 1800 may be provided a large number of referenceregions in a short period of time, it can efficiently and preciselyupdate the weights for any given application (e.g., an online game)being executed by the users.

As illustrated in FIG. 18B, the different host machines may generate newtrained weights (e.g., based on training/reference regions 1602 aspreviously described) and share the new trained weights with a server1800 (e.g., such as a gaming server) or, alternatively, use apeer-to-peer sharing protocol. A machine learning management component1810 on the server generates a set of combined weights 1805 using thenew weights received from each of the host machines. The combinedweights 1805, for example, may be an average generated from the newweights and continually updated as described herein. Once generated,copies of the combined weights 1605A-C may be transmitted and stored oneach of the host machines 1820-1821 which may then use the combinedweights as described herein to perform de-noising operations.

The semi-closed loop update mechanism can also be used by the hardwaremanufacturer. For example, the reference network may be included as partof the driver distributed by the hardware manufacturer. As the drivergenerates new training data using the techniques described herein andcontinuously submits these back to the hardware manufacturer, thehardware manufacturer uses this information to continue to improve itsmachine learning implementations for the next driver update.

In an example implementation (e.g., in batch movie rendering on a renderfarm), the renderer transmits the newly generated training regions to adedicated server or database (in that studio's render farm) thataggregates this data from multiple render nodes over time. A separateprocess on a separate machine continuously improves the studio'sdedicated denoising network, and new render jobs always use the latesttrained network.

A machine-learning method is illustrated in FIG. 19 . The method may beimplemented on the architectures described herein, but is not limited toany particular system or graphics processing architecture.

At 1901, as part of the initial training phase, low sample count imagedata and high sample count image data are generated for a plurality ofimage frames. At 1902, a machine-learning denoising engine is trainedusing the high/low sample count image data. For example, a set ofconvolutional neural network weights associated with pixel features maybe updated in accordance with the training. However, anymachine-learning architecture may be used.

At 1903, at runtime, low sample count image frames are generated alongwith at least one reference region having a high sample count. At 1904,the high sample count reference region is used by the machine-learningengine and/or separate training logic (e.g., background training module1700) to continually refine the training of the machine learning engine.For example, the high sample count reference region may be used incombination with a corresponding portion of the low sample count imageto continue to teach the machine learning engine 1904 how to mosteffectively perform denoising. In a CNN implementation, for example,this may involve updating the weights associated with the CNN.

Multiple variations described above may be implemented, such as themanner in which the feedback loop to the machine learning engine isconfigured, the entities which generate the training data, the manner inwhich the training data is fed back to training engine, and how theimproved network is provided to the rendering engines. In addition,while the examples described above perform continuous training using asingle reference region, any number of reference regions may be used.Moreover, as previously mentioned, the reference regions may be ofdifferent sizes, may be used on different numbers of image frames, andmay be positioned in different locations within the image frames usingdifferent techniques (e.g., random, according to a predeterminedpattern, etc).

In addition, while a convolutional neural network (CNN) is described asone example of a machine-learning engine 1600, the underlying principlesof the invention may be implemented using any form of machine learningengine which is capable of continually refining its results using newtraining data. By way of example, and not limitation, other machinelearning implementations include the group method of data handling(GMDH), long short-term memory, deep reservoir computing, deep beliefnetworks, tensor deep stacking networks, and deep predictive codingnetworks, to name a few.

Apparatus and Method for Efficient Distributed Denoising

As described above, denoising has become a critical feature forreal-time ray tracing with smooth, noiseless images. Rendering can bedone across a distributed system on multiple devices, but so far theexisting denoising frameworks all operate on a single instance on asingle machine. If rendering is being done across multiple devices, theymay not have all rendered pixels accessible for computing a denoisedportion of the image.

A distributed denoising algorithm that works with both artificialintelligence (AI) and non-AI based denoising techniques is presented.Regions of the image are either already distributed across nodes from adistributed render operation, or split up and distributed from a singleframebuffer. Ghost regions of neighboring regions needed for computingsufficient denoising are collected from neighboring nodes when needed,and the final resulting tiles are composited into a final image.

Distributed Processing

FIG. 20 illustrates multiple nodes 2021-2023 that perform rendering.While only three nodes are illustrated for simplicity, the underlyingprinciples of the invention are not limited to any particular number ofnodes. In fact, a single node may be used to implement certainembodiments of the invention.

Nodes 2021-2023 each render a portion of an image, resulting in regions2011-2013 in this example. While rectangular regions 2011-2013 are shownin FIG. 20 , regions of any shape may be used and any device can processany number of regions. The regions that are needed by a node to performa sufficiently smooth denoising operation are referred to as ghostregions 2011-2013. In other words, the ghost regions 2001-2003 representthe entirety of data required to perform denoising at a specified levelof quality. Lowering the quality level reduces the size of the ghostregion and therefore the amount of data required and raising the qualitylevel increases the ghost region and corresponding data required.

If a node such as node 2021 does have a local copy of a portion of theghost region 2001 required to denoise its region 2011 at a specifiedlevel of quality, the node will retrieve the required data from one ormore “adjacent” nodes, such as node 2022 which owns a portion of ghostregion 2001 as illustrated. Similarly, if node 2022 does have a localcopy of a portion of ghost region 2002 required to denoise its region2012 at the specified level of quality, node 2022 will retrieve therequired ghost region data 2032 from node 2021. The retrieval may beperformed over a bus, an interconnect, a high speed memory fabric, anetwork (e.g., high speed Ethernet), or may even be an on-chipinterconnect in a multi-core chip capable of distributing rendering workamong a plurality of cores (e.g., used for rendering large images ateither extreme resolutions or time varying). Each node 2021-2023 maycomprise an individual execution unit or specified set of executionunits within a graphics processor.

The specific amount of data to be sent is dependent on the denoisingtechniques being used. Moreover, the data from the ghost region mayinclude any data needed to improve denoising of each respective region.For example, the ghost region data may include image colors/wavelengths,intensity/alpha data, and/or normals. However, the underlying principlesof the invention are not limited to any particular set of ghost regiondata.

Additional Details

For slower networks or interconnects, compression of this data can beutilized using existing general purpose lossless or lossy compression.Examples include, but are not limited to, zlib, gzip, andLempel-Ziv-Markov chain algorithm (LZMA). Further content-specificcompression may be used by noting that the delta in ray hit informationbetween frames can be quite sparse, and only the samples that contributeto that delta need to be sent when the node already has the collecteddeltas from previous frames. These can be selectively pushed to nodesthat collect those samples, i, or node i can request samples from othernodes. Lossless compression is used for certain types of data andprogram code while lossy data is used for other types of data.

FIG. 21 illustrates additional details of the interactions between nodes2021-2022. Each node 2021-2022 includes a ray tracing renderingcircuitry 2081-2082 for rendering the respective image regions 2011-2012and ghost regions 2001-2002. Denoisers 2100-2111 execute denoisingoperations on the regions 2011-2012, respectively, which each node2021-2022 is responsible for rendering and denoising. The denoisers2021-2022, for example, may comprise circuitry, software, or anycombination thereof to generate the denoised regions 2121-2122,respectively. As mentioned, when generating denoised regions thedenoisers 2021-2022 may need to rely on data within a ghost region ownedby a different node (e.g., denoiser 2100 may need data from ghost region2002 owned by node 2022).

Thus, the denoisers 2100-2111 may generate the denoised regions2121-2122 using data from regions 2011-2012 and ghost regions 2001-2002,respectively, at least a portion of which may be received from anothernode. Region data managers 2101-2102 may manage data transfers fromghost regions 2001-2002 as described herein. Compressor/decompressorunits 2131-2132 may perform compression and decompression of the ghostregion data exchanged between the nodes 2021-2022, respectively.

For example, region data manager 2101 of node 2021 may, upon requestfrom node 2022, send data from ghost region 2001 tocompressor/decompressor 2131, which compresses the data to generatecompressed data 2106 which it transmits to node 2022, thereby reducingbandwidth over the interconnect, network, bus, or other datacommunication link. Compressor/decompressor 2132 of node 2022 thendecompresses the compressed data 2106 and denoiser 2111 uses thedecompressed ghost data to generate a higher quality denoised region2012 than would be possible with only data from region 2012. The regiondata manager 2102 may store the decompressed data from ghost region 2001in a cache, memory, register file or other storage to make it availableto the denoiser 2111 when generating the denoised region 2122. A similarset of operations may be performed to provide the data from ghost region2002 to denoiser 2100 on node 2021 which uses the data in combinationwith data from region 2011 to generate a higher quality denoised region2121.

Grab Data or Render

If the connection between devices such as nodes 2021-2022 is slow (i.e.,lower than a threshold latency and/or threshold bandwidth), it may befaster to render ghost regions locally rather than requesting theresults from other devices. This can be determined at run-time bytracking network transaction speeds and linearly extrapolated rendertimes for the ghost region size. In such cases where it is faster torender out the entire ghost region, multiple devices may end uprendering the same portions of the image. The resolution of the renderedportion of the ghost regions may be adjusted based on the variance ofthe base region and the determined degree of blurring.

Load Balancing

Static and/or dynamic load balancing schemes may be used to distributethe processing load among the various nodes 2021-2023. For dynamic loadbalancing, the variance determined by the denoising filter may requireboth more time in denoising but drive the amount of samples used torender a particular region of the scene, with low variance and blurryregions of the image requiring fewer samples. The specific regionsassigned to specific nodes may be adjusted dynamically based on datafrom previous frames or dynamically communicated across devices as theyare rendering so that all devices will have the same amount of work.

FIG. 22 illustrates how a monitor 2201-2202 running on each respectivenode 2021-2022 collects performance metric data including, but notlimited to, the time consumed to transmit data over the networkinterface 2211-2212, the time consumed when denoising a region (with andwithout ghost region data), and the time consumed rendering eachregion/ghost region. The monitors 2201-2202 report these performancemetrics back to a manager or load balancer node 2201, which analyzes thedata to identify the current workload on each node 2021-2022 andpotentially determines a more efficient mode of processing the variousdenoised regions 2121-2122. The manager node 2201 then distributes newworkloads for new regions to the nodes 2021-2022 in accordance with thedetected load. For example, the manager node 2201 may transmit more workto those nodes which are not heavily loaded and/or reallocate work fromthose nodes which are overloaded. In addition, the load balancer node2201 may transmit a reconfiguration command to adjust the specificmanner in which rendering and/or denoising is performed by each of thenodes (some examples of which are described above).

Determining Ghost Regions

The sizes and shapes of the ghost regions 2001-2002 may be determinedbased on the denoising algorithm implemented by the denoisers 2100-2111.Their respective sizes can then be dynamically modified based on thedetected variance of the samples being denoised. The learning algorithmused for AI denoising itself may be used for determining appropriateregion sizes, or in other cases such as a bilateral blur thepredetermined filter width will determine the size of the ghost regions2001-2002. In an exemplary implementation which uses a learningalgorithm, the machine learning engine may be executed on the managernode 2201 and/or portions of the machine learning may be executed oneach of the individual nodes 2021-2023 (see, e.g., FIGS. 18A-B andassociated text above).

Gathering the Final Image

The final image may be generated by gathering the rendered and denoisedregions from each of the nodes 2021-2023, without the need for the ghostregions or normals. In FIG. 22 , for example, the denoised regions2121-2122 are transmitted to regions processor 2280 of the manager node2201 which combines the regions to generate the final denoised image2290, which is then displayed on a display 2290. The region processor2280 may combine the regions using a variety of 2D compositingtechniques. Although illustrated as separate components, the regionprocessor 2280 and denoised image 2290 may be integral to the display2290. The various nodes 2021-2022 may use a direct-send technique totransmit the denoised regions 2121-2122 and potentially using variouslossy or lossless compression of the region data.

AI denoising is still a costly operation and as gaming moves into thecloud. As such, distributing processing of denoising across multiplenodes 2021-2022 may become required for achieving real-time frame ratesfor traditional gaming or virtual reality (VR) which requires higherframe rates. Movie studios also often render in large render farms whichcan be utilized for faster denoising.

An exemplary method for performing distributed rendering and denoisingis illustrated in FIG. 23 . The method may be implemented within thecontext of the system architectures described above, but is not limitedto any particular system architecture.

At 2301, graphics work is dispatched to a plurality of nodes whichperform ray tracing operations to render a region of an image frame.Each node may already have data required to perform the operations inmemory. For example, two or more of the nodes may share a common memoryor the local memories of the nodes may already have stored data fromprior ray tracing operations. Alternatively, or in addition, certaindata may be transmitted to each node.

At 2302, the “ghost region” required for a specified level of denoising(i.e., at an acceptable level of performance) is determined. The ghostregion comprises any data required to perform the specified level ofdenoising, including data owned by one or more other nodes.

At 2303, data related to the ghost regions (or portions thereof) isexchanged between nodes. At 2304 each node performs denoising on itsrespective region (e.g., using the exchanged data) and at 2305 theresults are combined to generate the final denoised image frame.

A manager node or primary node such as shown in FIG. 22 may dispatchethe work to the nodes and then combine the work performed by the nodesto generate the final image frame. A peer-based architecture can be usedwhere the nodes are peers which exchange data to render and denoise thefinal image frame.

The nodes described herein (e.g., nodes 2021-2023) may be graphicsprocessing computing systems interconnected via a high speed network.Alternatively, the nodes may be individual processing elements coupledto a high speed memory fabric. All of the nodes may share a commonvirtual memory space and/or a common physical memory.

Alternatively, the nodes may be a combination of CPUs and GPUs. Forexample, the manager node 2201 described above may be a CPU and/orsoftware executed on the CPU and the nodes 2021-2022 may be GPUs and/orsoftware executed on the GPUs. Various different types of nodes may beused while still complying with the underlying principles of theinvention.

Example Neural Network Implementations

There are many types of neural networks; a simple type of neural networkis a feedforward network. A feedforward network may be implemented as anacyclic graph in which the nodes are arranged in layers. Typically, afeedforward network topology includes an input layer and an output layerthat are separated by at least one hidden layer. The hidden layertransforms input received by the input layer into a representation thatis useful for generating output in the output layer. The network nodesare fully connected via edges to the nodes in adjacent layers, but thereare no edges between nodes within each layer. Data received at the nodesof an input layer of a feedforward network are propagated (i.e., “fedforward”) to the nodes of the output layer via an activation functionthat calculates the states of the nodes of each successive layer in thenetwork based on coefficients (“weights”) respectively associated witheach of the edges connecting the layers. Depending on the specific modelbeing represented by the algorithm being executed, the output from theneural network algorithm can take various forms.

Before a machine learning algorithm can be used to model a particularproblem, the algorithm is trained using a training data set. Training aneural network involves selecting a network topology, using a set oftraining data representing a problem being modeled by the network, andadjusting the weights until the network model performs with a minimalerror for all instances of the training data set. For example, during asupervised learning training process for a neural network, the outputproduced by the network in response to the input representing aninstance in a training data set is compared to the “correct” labeledoutput for that instance, an error signal representing the differencebetween the output and the labeled output is calculated, and the weightsassociated with the connections are adjusted to minimize that error asthe error signal is backward propagated through the layers of thenetwork. The network is considered “trained” when the errors for each ofthe outputs generated from the instances of the training data set areminimized.

The accuracy of a machine learning algorithm can be affectedsignificantly by the quality of the data set used to train thealgorithm. The training process can be computationally intensive and mayrequire a significant amount of time on a conventional general-purposeprocessor. Accordingly, parallel processing hardware is used to trainmany types of machine learning algorithms. This is particularly usefulfor optimizing the training of neural networks, as the computationsperformed in adjusting the coefficients in neural networks lendthemselves naturally to parallel implementations. Specifically, manymachine learning algorithms and software applications have been adaptedto make use of the parallel processing hardware within general-purposegraphics processing devices.

FIG. 24 is a generalized diagram of a machine learning software stack2400. A machine learning application 2402 can be configured to train aneural network using a training dataset or to use a trained deep neuralnetwork to implement machine intelligence. The machine learningapplication 2402 can include training and inference functionality for aneural network and/or specialized software that can be used to train aneural network before deployment. The machine learning application 2402can implement any type of machine intelligence including but not limitedto image recognition, mapping and localization, autonomous navigation,speech synthesis, medical imaging, or language translation.

Hardware acceleration for the machine learning application 2402 can beenabled via a machine learning framework 2404. The machine learningframework 2404 may be implemented on hardware described herein, such asthe processing system 100 comprising the processors and componentsdescribed herein. The elements described for FIG. 24 having the same orsimilar names as the elements of any other figure herein describe thesame elements as in the other figures, can operate or function in amanner similar to that, can comprise the same components, and can belinked to other entities, as those described elsewhere herein, but arenot limited to such. The machine learning framework 2404 can provide alibrary of machine learning primitives. Machine learning primitives arebasic operations that are commonly performed by machine learningalgorithms. Without the machine learning framework 2404, developers ofmachine learning algorithms would be required to create and optimize themain computational logic associated with the machine learning algorithm,then re-optimize the computational logic as new parallel processors aredeveloped. Instead, the machine learning application can be configuredto perform the necessary computations using the primitives provided bythe machine learning framework 2404. Exemplary primitives include tensorconvolutions, activation functions, and pooling, which are computationaloperations that are performed while training a convolutional neuralnetwork (CNN). The machine learning framework 2404 can also provideprimitives to implement basic linear algebra subprograms performed bymany machine-learning algorithms, such as matrix and vector operations.

The machine learning framework 2404 can process input data received fromthe machine learning application 2402 and generate the appropriate inputto a compute framework 2406. The compute framework 2406 can abstract theunderlying instructions provided to the GPGPU driver 2408 to enable themachine learning framework 2404 to take advantage of hardwareacceleration via the GPGPU hardware 2410 without requiring the machinelearning framework 2404 to have intimate knowledge of the architectureof the GPGPU hardware 2410. Additionally, the compute framework 2406 canenable hardware acceleration for the machine learning framework 2404across a variety of types and generations of the GPGPU hardware 2410.

GPGPU Machine Learning Acceleration

FIG. 25 illustrates a multi-GPU computing system 2500, which may be avariant of the processing system 100. Therefore, the disclosure of anyfeatures in combination with the processing system 100 herein alsodiscloses a corresponding combination with multi-GPU computing system2500, but is not limited to such. The elements of FIG. 25 having thesame or similar names as the elements of any other figure hereindescribe the same elements as in the other figures, can operate orfunction in a manner similar to that, can comprise the same components,and can be linked to other entities, as those described elsewhereherein, but are not limited to such. The multi-GPU computing system 2500can include a processor 2502 coupled to multiple GPGPUs 2506A-D via ahost interface switch 2504. The host interface switch 2504 may forexample be a PCI express switch device that couples the processor 2502to a PCI express bus over which the processor 2502 can communicate withthe set of GPGPUs 2506A-D. Each of the multiple GPGPUs 2506A-D can be aninstance of the GPGPU described above. The GPGPUs 2506A-D caninterconnect via a set of high-speed point to point GPU to GPU links2516. The high-speed GPU to GPU links can connect to each of the GPGPUs2506A-D via a dedicated GPU link. The P2P GPU links 2516 enable directcommunication between each of the GPGPUs 2506A-D without requiringcommunication over the host interface bus to which the processor 2502 isconnected. With GPU-to-GPU traffic directed to the P2P GPU links, thehost interface bus remains available for system memory access or tocommunicate with other instances of the multi-GPU computing system 2500,for example, via one or more network devices. Instead of connecting theGPGPUs 2506A-D to the processor 2502 via the host interface switch 2504,the processor 2502 can include direct support for the P2P GPU links 2516and, thus, connect directly to the GPGPUs 2506A-D.

Machine Learning Neural Network Implementations

The computing architecture described herein can be configured to performthe types of parallel processing that is particularly suited fortraining and deploying neural networks for machine learning. A neuralnetwork can be generalized as a network of functions having a graphrelationship. As is well-known in the art, there are a variety of typesof neural network implementations used in machine learning. Oneexemplary type of neural network is the feedforward network, aspreviously described.

A second exemplary type of neural network is the Convolutional NeuralNetwork (CNN). A CNN is a specialized feedforward neural network forprocessing data having a known, grid-like topology, such as image data.Accordingly, CNNs are commonly used for compute vision and imagerecognition applications, but they also may be used for other types ofpattern recognition such as speech and language processing. The nodes inthe CNN input layer are organized into a set of “filters” (featuredetectors inspired by the receptive fields found in the retina), and theoutput of each set of filters is propagated to nodes in successivelayers of the network. The computations for a CNN include applying theconvolution mathematical operation to each filter to produce the outputof that filter. Convolution is a specialized kind of mathematicaloperation performed by two functions to produce a third function that isa modified version of one of the two original functions. Inconvolutional network terminology, the first function to the convolutioncan be referred to as the input, while the second function can bereferred to as the convolution kernel. The output may be referred to asthe feature map. For example, the input to a convolution layer can be amultidimensional array of data that defines the various color componentsof an input image. The convolution kernel can be a multidimensionalarray of parameters, where the parameters are adapted by the trainingprocess for the neural network.

Recurrent neural networks (RNNs) are a family of feedforward neuralnetworks that include feedback connections between layers. RNNs enablemodeling of sequential data by sharing parameter data across differentparts of the neural network. The architecture for a RNN includes cycles.The cycles represent the influence of a present value of a variable onits own value at a future time, as at least a portion of the output datafrom the RNN is used as feedback for processing subsequent input in asequence. This feature makes RNNs particularly useful for languageprocessing due to the variable nature in which language data can becomposed.

The figures described below present exemplary feedforward, CNN, and RNNnetworks, as well as describe a general process for respectivelytraining and deploying each of those types of networks. It will beunderstood that these descriptions are exemplary and non-limiting andthe concepts illustrated can be applied generally to deep neuralnetworks and machine learning techniques in general.

The exemplary neural networks described above can be used to performdeep learning. Deep learning is machine learning using deep neuralnetworks. The deep neural networks used in deep learning are artificialneural networks composed of multiple hidden layers, as opposed toshallow neural networks that include only a single hidden layer. Deeperneural networks are generally more computationally intensive to train.However, the additional hidden layers of the network enable multisteppattern recognition that results in reduced output error relative toshallow machine learning techniques.

Deep neural networks used in deep learning typically include a front-endnetwork to perform feature recognition coupled to a back-end networkwhich represents a mathematical model that can perform operations (e.g.,object classification, speech recognition, etc.) based on the featurerepresentation provided to the model. Deep learning enables machinelearning to be performed without requiring hand crafted featureengineering to be performed for the model. Instead, deep neural networkscan learn features based on statistical structure or correlation withinthe input data. The learned features can be provided to a mathematicalmodel that can map detected features to an output. The mathematicalmodel used by the network is generally specialized for the specific taskto be performed, and different models will be used to perform differenttask.

Once the neural network is structured, a learning model can be appliedto the network to train the network to perform specific tasks. Thelearning model describes how to adjust the weights within the model toreduce the output error of the network. Backpropagation of errors is acommon method used to train neural networks. An input vector ispresented to the network for processing. The output of the network iscompared to the desired output using a loss function and an error valueis calculated for each of the neurons in the output layer. The errorvalues are then propagated backwards until each neuron has an associatederror value which roughly represents its contribution to the originaloutput. The network can then learn from those errors using an algorithm,such as the stochastic gradient descent algorithm, to update the weightsof the of the neural network.

FIGS. 26-27 illustrate an exemplary convolutional neural network. FIG.26 illustrates various layers within a CNN. As shown in FIG. 26 , anexemplary CNN used to model image processing can receive input 2602describing the red, green, and blue (RGB) components of an input image.The input 2602 can be processed by multiple convolutional layers (e.g.,convolutional layer 2604, convolutional layer 2606). The output from themultiple convolutional layers may optionally be processed by a set offully connected layers 2608. Neurons in a fully connected layer havefull connections to all activations in the previous layer, as previouslydescribed for a feedforward network. The output from the fully connectedlayers 2608 can be used to generate an output result from the network.The activations within the fully connected layers 2608 can be computedusing matrix multiplication instead of convolution. Not all CNNimplementations make use of fully connected layers. For example, in someimplementations the convolutional layer 2606 can generate output for theCNN.

The convolutional layers are sparsely connected, which differs fromtraditional neural network configuration found in the fully connectedlayers 2608. Traditional neural network layers are fully connected, suchthat every output unit interacts with every input unit. However, theconvolutional layers are sparsely connected because the output of theconvolution of a field is input (instead of the respective state valueof each of the nodes in the field) to the nodes of the subsequent layer,as illustrated. The kernels associated with the convolutional layersperform convolution operations, the output of which is sent to the nextlayer. The dimensionality reduction performed within the convolutionallayers is one aspect that enables the CNN to scale to process largeimages.

FIG. 27 illustrates exemplary computation stages within a convolutionallayer of a CNN. Input to a convolutional layer 2712 of a CNN can beprocessed in three stages of a convolutional layer 2714. The threestages can include a convolution stage 2716, a detector stage 2718, anda pooling stage 2720. The convolution layer 2714 can then output data toa successive convolutional layer. The final convolutional layer of thenetwork can generate output feature map data or provide input to a fullyconnected layer, for example, to generate a classification value for theinput to the CNN.

In the convolution stage 2716 performs several convolutions in parallelto produce a set of linear activations. The convolution stage 2716 caninclude an affine transformation, which is any transformation that canbe specified as a linear transformation plus a translation. Affinetransformations include rotations, translations, scaling, andcombinations of these transformations. The convolution stage computesthe output of functions (e.g., neurons) that are connected to specificregions in the input, which can be determined as the local regionassociated with the neuron. The neurons compute a dot product betweenthe weights of the neurons and the region in the local input to whichthe neurons are connected. The output from the convolution stage 2716defines a set of linear activations that are processed by successivestages of the convolutional layer 2714.

The linear activations can be processed by a detector stage 2718. In thedetector stage 2718, each linear activation is processed by a non-linearactivation function. The non-linear activation function increases thenonlinear properties of the overall network without affecting thereceptive fields of the convolution layer. Several types of non-linearactivation functions may be used. One particular type is the rectifiedlinear unit (ReLU), which uses an activation function defined asf(x)=max (0,x), such that the activation is thresholded at zero.

The pooling stage 2720 uses a pooling function that replaces the outputof the convolutional layer 2706 with a summary statistic of the nearbyoutputs. The pooling function can be used to introduce translationinvariance into the neural network, such that small translations to theinput do not change the pooled outputs. Invariance to local translationcan be useful in scenarios where the presence of a feature in the inputdata is more important than the precise location of the feature. Varioustypes of pooling functions can be used during the pooling stage 2720,including max pooling, average pooling, and 12-norm pooling.Additionally, some CNN implementations do not include a pooling stage.Instead, such implementations substitute and additional convolutionstage having an increased stride relative to previous convolutionstages.

The output from the convolutional layer 2714 can then be processed bythe next layer 2722. The next layer 2722 can be an additionalconvolutional layer or one of the fully connected layers 2708. Forexample, the first convolutional layer 2704 of FIG. 27 can output to thesecond convolutional layer 2706, while the second convolutional layercan output to a first layer of the fully connected layers 2808.

FIG. 28 illustrates an exemplary recurrent neural network 2800. In arecurrent neural network (RNN), the previous state of the networkinfluences the output of the current state of the network. RNNs can bebuilt in a variety of ways using a variety of functions. The use of RNNsgenerally revolves around using mathematical models to predict thefuture based on a prior sequence of inputs. For example, an RNN may beused to perform statistical language modeling to predict an upcomingword given a previous sequence of words. The illustrated RNN 2800 can bedescribed has having an input layer 2802 that receives an input vector,hidden layers 2804 to implement a recurrent function, a feedbackmechanism 2805 to enable a ‘memory’ of previous states, and an outputlayer 2806 to output a result. The RNN 2800 operates based ontime-steps. The state of the RNN at a given time step is influencedbased on the previous time step via the feedback mechanism 2805. For agiven time step, the state of the hidden layers 2804 is defined by theprevious state and the input at the current time step. An initial input(x1) at a first time step can be processed by the hidden layer 2804. Asecond input (x2) can be processed by the hidden layer 2804 using stateinformation that is determined during the processing of the initialinput (x1). A given state can be computed as s_t=f(Ux_t+Ws_(t−1)), whereU and W are parameter matrices. The function f is generally anonlinearity, such as the hyperbolic tangent function (Tanh) or avariant of the rectifier function f(x)=max (0,x). However, the specificmathematical function used in the hidden layers 2804 can vary dependingon the specific implementation details of the RNN 2800.

In addition to the basic CNN and RNN networks described, variations onthose networks may be enabled. One example RNN variant is the long shortterm memory (LSTM) RNN. LSTM RNNs are capable of learning long-termdependencies that may be necessary for processing longer sequences oflanguage. A variant on the CNN is a convolutional deep belief network,which has a structure similar to a CNN and is trained in a mannersimilar to a deep belief network. A deep belief network (DBN) is agenerative neural network that is composed of multiple layers ofstochastic (random) variables. DBNs can be trained layer-by-layer usinggreedy unsupervised learning. The learned weights of the DBN can then beused to provide pre-train neural networks by determining an optimalinitial set of weights for the neural network.

FIG. 29 illustrates training and deployment of a deep neural network.Once a given network has been structured for a task the neural networkis trained using a training dataset 2902. Various training frameworks2904 have been developed to enable hardware acceleration of the trainingprocess. For example, the machine learning framework described above maybe configured as a training framework. The training framework 2904 canhook into an untrained neural network 2906 and enable the untrainedneural net to be trained using the parallel processing resourcesdescribed herein to generate a trained neural net 2908.

To start the training process the initial weights may be chosen randomlyor by pre-training using a deep belief network. The training cycle thenbe performed in either a supervised or unsupervised manner.

Supervised learning is a learning method in which training is performedas a mediated operation, such as when the training dataset 2902 includesinput paired with the desired output for the input, or where thetraining dataset includes input having known output and the output ofthe neural network is manually graded. The network processes the inputsand compares the resulting outputs against a set of expected or desiredoutputs. Errors are then propagated back through the system. Thetraining framework 2904 can adjust to adjust the weights that controlthe untrained neural network 2906. The training framework 2904 canprovide tools to monitor how well the untrained neural network 2906 isconverging towards a model suitable to generating correct answers basedon known input data. The training process occurs repeatedly as theweights of the network are adjusted to refine the output generated bythe neural network. The training process can continue until the neuralnetwork reaches a statistically desired accuracy associated with atrained neural net 2908. The trained neural network 2908 can then bedeployed to implement any number of machine learning operations.

Unsupervised learning is a learning method in which the network attemptsto train itself using unlabeled data. Thus, for unsupervised learningthe training dataset 2902 will include input data without any associatedoutput data. The untrained neural network 2906 can learn groupingswithin the unlabeled input and can determine how individual inputs arerelated to the overall dataset. Unsupervised training can be used togenerate a self-organizing map, which is a type of trained neuralnetwork 2907 capable of performing operations useful in reducing thedimensionality of data. Unsupervised training can also be used toperform anomaly detection, which allows the identification of datapoints in an input dataset that deviate from the normal patterns of thedata.

Variations on supervised and unsupervised training may also be employed.Semi-supervised learning is a technique in which in the training dataset2902 includes a mix of labeled and unlabeled data of the samedistribution. Incremental learning is a variant of supervised learningin which input data is continuously used to further train the model.Incremental learning enables the trained neural network 2908 to adapt tothe new data 2912 without forgetting the knowledge instilled within thenetwork during initial training.

Whether supervised or unsupervised, the training process forparticularly deep neural networks may be too computationally intensivefor a single compute node. Instead of using a single compute node, adistributed network of computational nodes can be used to accelerate thetraining process.

FIG. 30A is a block diagram illustrating distributed learning.Distributed learning is a training model that uses multiple distributedcomputing nodes such as the nodes described above to perform supervisedor unsupervised training of a neural network. The distributedcomputational nodes can each include one or more host processors and oneor more of the general-purpose processing nodes, such as ahighly-parallel general-purpose graphics processing unit. Asillustrated, distributed learning can be performed model parallelism3002, data parallelism 3004, or a combination of model and dataparallelism.

In model parallelism 3002, different computational nodes in adistributed system can perform training computations for different partsof a single network. For example, each layer of a neural network can betrained by a different processing node of the distributed system. Thebenefits of model parallelism include the ability to scale toparticularly large models. Splitting the computations associated withdifferent layers of the neural network enables the training of verylarge neural networks in which the weights of all layers would not fitinto the memory of a single computational node. In some instances, modelparallelism can be particularly useful in performing unsupervisedtraining of large neural networks.

In data parallelism 3004, the different nodes of the distributed networkhave a complete instance of the model and each node receives a differentportion of the data. The results from the different nodes are thencombined. While different approaches to data parallelism are possible,data parallel training approaches all require a technique of combiningresults and synchronizing the model parameters between each node.Exemplary approaches to combining data include parameter averaging andupdate based data parallelism. Parameter averaging trains each node on asubset of the training data and sets the global parameters (e.g.,weights, biases) to the average of the parameters from each node.Parameter averaging uses a central parameter server that maintains theparameter data. Update based data parallelism is similar to parameteraveraging except that instead of transferring parameters from the nodesto the parameter server, the updates to the model are transferred.Additionally, update based data parallelism can be performed in adecentralized manner, where the updates are compressed and transferredbetween nodes.

Combined model and data parallelism 3006 can be implemented, forexample, in a distributed system in which each computational nodeincludes multiple GPUs. Each node can have a complete instance of themodel with separate GPUs within each node are used to train differentportions of the model.

Distributed training has increased overhead relative to training on asingle machine. However, the parallel processors and GPGPUs describedherein can each implement various techniques to reduce the overhead ofdistributed training, including techniques to enable high bandwidthGPU-to-GPU data transfer and accelerated remote data synchronization.

Exemplary Machine Learning Applications

Machine learning can be applied to solve a variety of technologicalproblems, including but not limited to computer vision, autonomousdriving and navigation, speech recognition, and language processing.Computer vision has traditionally been one of the most active researchareas for machine learning applications. Applications of computer visionrange from reproducing human visual abilities, such as recognizingfaces, to creating new categories of visual abilities. For example,computer vision applications can be configured to recognize sound wavesfrom the vibrations induced in objects visible in a video. Parallelprocessor accelerated machine learning enables computer visionapplications to be trained using significantly larger training datasetthan previously feasible and enables inferencing systems to be deployedusing low power parallel processors.

Parallel processor accelerated machine learning has autonomous drivingapplications including lane and road sign recognition, obstacleavoidance, navigation, and driving control. Accelerated machine learningtechniques can be used to train driving models based on datasets thatdefine the appropriate responses to specific training input. Theparallel processors described herein can enable rapid training of theincreasingly complex neural networks used for autonomous drivingsolutions and enables the deployment of low power inferencing processorsin a mobile platform suitable for integration into autonomous vehicles.

Parallel processor accelerated deep neural networks have enabled machinelearning approaches to automatic speech recognition (ASR). ASR includesthe creation of a function that computes the most probable linguisticsequence given an input acoustic sequence. Accelerated machine learningusing deep neural networks have enabled the replacement of the hiddenMarkov models (HMMs) and Gaussian mixture models (GMMs) previously usedfor ASR.

Parallel processor accelerated machine learning can also be used toaccelerate natural language processing. Automatic learning procedurescan make use of statistical inference algorithms to produce models thatare robust to erroneous or unfamiliar input. Exemplary natural languageprocessor applications include automatic machine translation betweenhuman languages.

The parallel processing platforms used for machine learning can bedivided into training platforms and deployment platforms. Trainingplatforms are generally highly parallel and include optimizations toaccelerate multi-GPU single node training and multi-node, multi-GPUtraining. Exemplary parallel processors suited for training include thehighly-parallel general-purpose graphics processing unit and/or themulti-GPU computing systems described herein. On the contrary, deployedmachine learning platforms generally include lower power parallelprocessors suitable for use in products such as cameras, autonomousrobots, and autonomous vehicles.

FIG. 30B illustrates an exemplary inferencing system on a chip (SOC)3100 suitable for performing inferencing using a trained model. Theelements of FIG. 30B having the same or similar names as the elements ofany other figure herein describe the same elements as in the otherfigures, can operate or function in a manner similar to that, cancomprise the same components, and can be linked to other entities, asthose described elsewhere herein, but are not limited to such. The SOC3100 can integrate processing components including a media processor3102, a vision processor 3104, a GPGPU 3106 and a multi-core processor3108. The SOC 3100 can additionally include on-chip memory 3105 that canenable a shared on-chip data pool that is accessible by each of theprocessing components. The processing components can be optimized forlow power operation to enable deployment to a variety of machinelearning platforms, including autonomous vehicles and autonomous robots.For example, one implementation of the SOC 3100 can be used as a portionof the main control system for an autonomous vehicle. Where the SOC 3100is configured for use in autonomous vehicles the SOC is designed andconfigured for compliance with the relevant functional safety standardsof the deployment jurisdiction.

During operation, the media processor 3102 and vision processor 3104 canwork in concert to accelerate computer vision operations. The mediaprocessor 3102 can enable low latency decode of multiple high-resolution(e.g., 4K, 8K) video streams. The decoded video streams can be writtento a buffer in the on-chip-memory 3105. The vision processor 3104 canthen parse the decoded video and perform preliminary processingoperations on the frames of the decoded video in preparation ofprocessing the frames using a trained image recognition model. Forexample, the vision processor 3104 can accelerate convolution operationsfor a CNN that is used to perform image recognition on thehigh-resolution video data, while back end model computations areperformed by the GPGPU 3106.

The multi-core processor 3108 can include control logic to assist withsequencing and synchronization of data transfers and shared memoryoperations performed by the media processor 3102 and the visionprocessor 3104. The multi-core processor 3108 can also function as anapplication processor to execute software applications that can make useof the inferencing compute capability of the GPGPU 3106. For example, atleast a portion of the navigation and driving logic can be implementedin software executing on the multi-core processor 3108. Such softwarecan directly issue computational workloads to the GPGPU 3106 or thecomputational workloads can be issued to the multi-core processor 3108,which can offload at least a portion of those operations to the GPGPU3106.

The GPGPU 3106 can include processing clusters such as a low powerconfiguration of the processing clusters DPLAB06A-DPLAB06H within thehighly-parallel general-purpose graphics processing unit DPLAB00. Theprocessing clusters within the GPGPU 3106 can support instructions thatare specifically optimized to perform inferencing computations on atrained neural network. For example, the GPGPU 3106 can supportinstructions to perform low precision computations such as 8-bit and4-bit integer vector operations.

RAY TRACING ARCHITECTURE

In one implementation, the graphics processor includes circuitry and/orprogram code for performing real-time ray tracing. A dedicated set ofray tracing cores may be included in the graphics processor to performthe various ray tracing operations described herein, including raytraversal and/or ray intersection operations. In addition to the raytracing cores, multiple sets of graphics processing cores for performingprogrammable shading operations and multiple sets of tensor cores forperforming matrix operations on tensor data may also be included.

FIG. 31 illustrates an exemplary portion of one such graphics processingunit (GPU) 3105 which includes dedicated sets of graphics processingresources arranged into multi-core groups 3100A-N. The graphicsprocessing unit (GPU) 3105 may be a variant of the graphics processor300, the GPGPU 1340 and/or any other graphics processor describedherein. Therefore, the disclosure of any features for graphicsprocessors also discloses a corresponding combination with the GPU 3105,but is not limited to such. Moreover, the elements of FIG. 31 having thesame or similar names as the elements of any other figure hereindescribe the same elements as in the other figures, can operate orfunction in a manner similar to that, can comprise the same components,and can be linked to other entities, as those described elsewhereherein, but are not limited to such. While the details of only a singlemulti-core group 3100A are provided, it will be appreciated that theother multi-core groups 3100B-N may be equipped with the same or similarsets of graphics processing resources.

As illustrated, a multi-core group 3100A may include a set of graphicscores 3130, a set of tensor cores 3140, and a set of ray tracing cores3150. A scheduler/dispatcher 3110 schedules and dispatches the graphicsthreads for execution on the various cores 3130, 3140, 3150. A set ofregister files 3120 store operand values used by the cores 3130, 3140,3150 when executing the graphics threads. These may include, forexample, integer registers for storing integer values, floating pointregisters for storing floating point values, vector registers forstoring packed data elements (integer and/or floating point dataelements) and tile registers for storing tensor/matrix values. The tileregisters may be implemented as combined sets of vector registers.

One or more Level 1 (L1) caches and texture units 3160 store graphicsdata such as texture data, vertex data, pixel data, ray data, boundingvolume data, etc, locally within each multi-core group 3100A. A Level 2(L2) cache 3180 shared by all or a subset of the multi-core groups3100A-N stores graphics data and/or instructions for multiple concurrentgraphics threads. As illustrated, the L2 cache 3180 may be shared acrossa plurality of multi-core groups 3100A-N. One or more memory controllers3170 couple the GPU 3105 to a memory 3198 which may be a system memory(e.g., DRAM) and/or a local graphics memory (e.g., GDDR6 memory).

Input/output (IO) circuitry 3195 couples the GPU 3105 to one or more IOdevices 3195 such as digital signal processors (DSPs), networkcontrollers, or user input devices. An on-chip interconnect may be usedto couple the I/O devices 3190 to the GPU 3105 and memory 3198. One ormore 10 memory management units (IOMMUs) 3170 of the IO circuitry 3195couple the IO devices 3190 directly to the system memory 3198. The IOMMU3170 may manage multiple sets of page tables to map virtual addresses tophysical addresses in system memory 3198. Additionally, the IO devices3190, CPU(s) 3199, and GPU(s) 3105 may share the same virtual addressspace.

The IOMMU 3170 may also support virtualization. In this case, it maymanage a first set of page tables to map guest/graphics virtualaddresses to guest/graphics physical addresses and a second set of pagetables to map the guest/graphics physical addresses to system/hostphysical addresses (e.g., within system memory 3198). The base addressesof each of the first and second sets of page tables may be stored incontrol registers and swapped out on a context switch (e.g., so that thenew context is provided with access to the relevant set of page tables).While not illustrated in FIG. 31 , each of the cores 3130, 3140, 3150and/or multi-core groups 3100A-N may include translation lookasidebuffers (TLBs) to cache guest virtual to guest physical translations,guest physical to host physical translations, and guest virtual to hostphysical translations.

The CPUs 3199, GPUs 3105, and IO devices 3190 can be integrated on asingle semiconductor chip and/or chip package. The illustrated memory3198 may be integrated on the same chip or may be coupled to the memorycontrollers 3170 via an off-chip interface. In one implementation, thememory 3198 comprises GDDR6 memory which shares the same virtual addressspace as other physical system-level memories, although the underlyingprinciples of the invention are not limited to this specificimplementation.

The tensor cores 3140 may include a plurality of execution unitsspecifically designed to perform matrix operations, which are thefundamental compute operation used to perform deep learning operations.For example, simultaneous matrix multiplication operations may be usedfor neural network training and inferencing. The tensor cores 3140 mayperform matrix processing using a variety of operand precisionsincluding single precision floating-point (e.g., 32 bits),half-precision floating point (e.g., 16 bits), integer words (16 bits),bytes (8 bits), and half-bytes (4 bits). A neural network implementationmay also extract features of each rendered scene, potentially combiningdetails from multiple frames, to construct a high-quality final image.

In deep learning implementations, parallel matrix multiplication workmay be scheduled for execution on the tensor cores 3140. The training ofneural networks, in particular, requires a significant number matrix dotproduct operations. In order to process an inner-product formulation ofanN×N×N matrix multiply, the tensor cores 3140 may include at least Ndot-product processing elements. Before the matrix multiply begins, oneentire matrix is loaded into tile registers and at least one column of asecond matrix is loaded each cycle for N cycles. Each cycle, there are Ndot products that are processed.

Matrix elements may be stored at different precisions depending on theparticular implementation, including 16-bit words, 8-bit bytes (e.g.,INT8) and 4-bit half-bytes (e.g., INT4). Different precision modes maybe specified for the tensor cores 3140 to ensure that the most efficientprecision is used for different workloads (e.g., such as inferencingworkloads which can tolerate quantization to bytes and half-bytes).

The ray tracing cores 3150 may be used to accelerate ray tracingoperations for both real-time ray tracing and non-real-time ray tracingimplementations. In particular, the ray tracing cores 3150 may includeray traversal/intersection circuitry for performing ray traversal usingbounding volume hierarchies (BVHs) and identifying intersections betweenrays and primitives enclosed within the BVH volumes. The ray tracingcores 3150 may also include circuitry for performing depth testing andculling (e.g., using a Z buffer or similar arrangement). In oneimplementation, the ray tracing cores 3150 perform traversal andintersection operations in concert with the image denoising techniquesdescribed herein, at least a portion of which may be executed on thetensor cores 3140. For example, the tensor cores 3140 may implement adeep learning neural network to perform denoising of frames generated bythe ray tracing cores 3150. However, the CPU(s) 3199, graphics cores3130, and/or ray tracing cores 3150 may also implement all or a portionof the denoising and/or deep learning algorithms.

In addition, as described above, a distributed approach to denoising maybe employed in which the GPU 3105 is in a computing device coupled toother computing devices over a network or high speed interconnect. Theinterconnected computing devices may additionally share neural networklearning/training data to improve the speed with which the overallsystem learns to perform denoising for different types of image framesand/or different graphics applications.

The ray tracing cores 3150 may process all BVH traversal andray-primitive intersections, saving the graphics cores 3130 from beingoverloaded with thousands of instructions per ray. Each ray tracing core3150 may include a first set of specialized circuitry for performingbounding box tests (e.g., for traversal operations) and a second set ofspecialized circuitry for performing the ray-triangle intersection tests(e.g., intersecting rays which have been traversed). Thus, themulti-core group 3100A can simply launch a ray probe, and the raytracing cores 3150 independently perform ray traversal and intersectionand return hit data (e.g., a hit, no hit, multiple hits, etc) to thethread context. The other cores 3130, 3140 may be freed to perform othergraphics or compute work while the ray tracing cores 3150 perform thetraversal and intersection operations.

Each ray tracing core 3150 may include a traversal unit to perform BVHtesting operations and an intersection unit which performs ray-primitiveintersection tests. The intersection unit may then generate a “hit”, “nohit”, or “multiple hit” response, which it provides to the appropriatethread. During the traversal and intersection operations, the executionresources of the other cores (e.g., graphics cores 3130 and tensor cores3140) may be freed to perform other forms of graphics work.

A hybrid rasterization/ray tracing approach may also be used in whichwork is distributed between the graphics cores 3130 and ray tracingcores 3150.

The ray tracing cores 3150 (and/or other cores 3130, 3140) may includehardware support for a ray tracing instruction set such as Microsoft'sDirectX Ray Tracing (DXR) which includes a DispatchRays command, as wellas ray-generation, closest-hit, any-hit, and miss shaders, which enablethe assignment of unique sets of shaders and textures for each object.Another ray tracing platform which may be supported by the ray tracingcores 3150, graphics cores 3130 and tensor cores 3140 is Vulkan 1.1.85.Note, however, that the underlying principles of the invention are notlimited to any particular ray tracing ISA.

In general, the various cores 3150, 3140, 3130 may support a ray tracinginstruction set that includes instructions/functions for ray generation,closest hit, any hit, ray-primitive intersection, per-primitive andhierarchical bounding box construction, miss, visit, and exceptions.More specifically, ray tracing instructions can be included to performthe following functions:

Ray Generation—Ray generation instructions may be executed for eachpixel, sample, or other user-defined work assignment.

Closest Hit—A closest hit instruction may be executed to locate theclosest intersection point of a ray with primitives within a scene.

Any Hit—An any hit instruction identifies multiple intersections betweena ray and primitives within a scene, potentially to identify a newclosest intersection point.

Intersection—An intersection instruction performs a ray-primitiveintersection test and outputs a result.

Per-primitive Bounding box Construction—This instruction builds abounding box around a given primitive or group of primitives (e.g., whenbuilding a new BVH or other acceleration data structure).

Miss—Indicates that a ray misses all geometry within a scene, orspecified region of a scene.

Visit—Indicates the children volumes a ray will traverse.

Exceptions—Includes various types of exception handlers (e.g., invokedfor various error conditions).

Hierarchical Beam Tracing

Bounding volume hierarchies are commonly used to improve the efficiencywith which operations are performed on graphics primitives and othergraphics objects. A BVH is a hierarchical tree structure which is builtbased on a set of geometric objects. At the top of the tree structure isthe root node which encloses all of the geometric objects in a givenscene. The individual geometric objects are wrapped in bounding volumesthat form the leaf nodes of the tree. These nodes are then grouped assmall sets and enclosed within larger bounding volumes. These, in turn,are also grouped and enclosed within other larger bounding volumes in arecursive fashion, eventually resulting in a tree structure with asingle bounding volume, represented by the root node, at the top of thetree. Bounding volume hierarchies are used to efficiently support avariety of operations on sets of geometric objects, such as collisiondetection, primitive culling, and ray traversal/intersection operationsused in ray tracing.

In ray tracing architectures, rays are traversed through a BVH todetermine ray-primitive intersections. For example, if a ray does notpass through the root node of the BVH, then the ray does not intersectany of the primitives enclosed by the BVH and no further processing isrequired for the ray with respect to this set of primitives. If a raypasses through a first child node of the BVH but not the second childnode, then the ray need not be tested against any primitives enclosed bythe second child node. In this manner, a BVH provides an efficientmechanism to test for ray-primitive intersections.

Groups of contiguous rays, referred to as “beams” may be tested againstthe BVH, rather than individual rays. FIG. 32 illustrates an exemplarybeam 3201 outlined by four different rays. Any rays which intersect thepatch 3200 defined by the four rays are considered to be within the samebeam. While the beam 3201 in FIG. 32 is defined by a rectangulararrangement of rays, beams may be defined in various other ways whilestill complying with the underlying principles of the invention (e.g.,circles, ellipses, etc).

FIG. 33 illustrates how a ray tracing engine 3310 of a GPU 3320implements the beam tracing techniques described herein. In particular,ray generation circuitry 3304 generates a plurality of rays for whichtraversal and intersection operations are to be performed. However,rather than performing traversal and intersection operations onindividual rays, traversal and intersection operations are performedusing a hierarchy of beams 3307 generated by beam hierarchy constructioncircuitry 3305. The beam hierarchy is analogous to the bounding volumehierarchy (BVH). For example, FIG. 34 provides an example of a primarybeam 3400 which may be subdivided into a plurality of differentcomponents. In particular, primary beam 3400 may be divided intoquadrants 3401-3404 and each quadrant may itself be divided intosub-quadrants such as sub-quadrants A-D within quadrant 3404. Theprimary beam may be subdivided in a variety of ways. For example, theprimary beam may be divided in half (rather than quadrants) and eachhalf may be divided in half, and so on. Regardless of how thesubdivisions are made, a hierarchical structure is generated in asimilar manner as a BVH, e.g., with a root node representing the primarybeam 3400, a first level of child nodes, each represented by a quadrant3401-3404, second level child nodes for each sub-quadrant A-D, and soon.

Once the beam hierarchy 3307 is constructed, traversal/intersectioncircuitry 3306 may perform traversal/intersection operations using thebeam hierarchy 3307 and the BVH 3308. In particular, it may test thebeam against the BVH and cull portions of the beam which do notintersect any portions of the BVH. Using the data shown in FIG. 34 , forexample, if the sub-beams associated with sub-regions 3402 and 3403 donot intersect with the BVH or a particular branch of the BVH, then theymay be culled with respect to the BVH or the branch. The remainingportions 3401, 3404 may be tested against the BVH by performing adepth-first search or other search algorithm.

A method for ray-tracing is illustrated in FIG. 35 . The method may beimplemented within the context of the graphics processing architecturesdescribed above, but is not limited to any particular architecture.

At 3500 a primary beam is constructed comprising a plurality of rays andat 3501, the beam is subdivided and hierarchical data structuresgenerated to create a beam hierarchy. The operations 3500-3501 may beperformed as a single, integrated operation which constructs a beamhierarchy from a plurality of rays. At 3502, the beam hierarchy is usedwith a BVH to cull rays (from the beam hierarchy) and/ornodes/primitives from the BVH. At 3503, ray-primitive intersections aredetermined for the remaining rays and primitives.

Lossy and Lossless Packet Compression in a Distributed Ray TracingSystem

Ray tracing operations may be distributed across a plurality of computenodes coupled together over a network. FIG. 36 , for example,illustrates a ray tracing cluster 3600 comprising a plurality of raytracing nodes 3610-3613 perform ray tracing operations in parallel,potentially combining the results on one of the nodes. In theillustrated architecture, the ray tracing nodes 3610-3613 arecommunicatively coupled to a client-side ray tracing application 3630via a gateway.

One of the difficulties with a distributed architecture is the largeamount of packetized data that must be transmitted between each of theray tracing nodes 3610-3613. Both lossless compression techniques andlossy compression techniques may be used to reduce the data transmittedbetween the ray tracing nodes 3610-3613.

To implement lossless compression, rather than sending packets filledwith the results of certain types of operations, data or commands aresent which allow the receiving node to reconstruct the results. Forexample, stochastically sampled area lights and ambient occlusion (AO)operations do not necessarily need directions. Consequently, atransmitting node can simply send a random seed which is then used bythe receiving node to perform random sampling. For example, if a sceneis distributed across nodes 3610-3612, to sample light 1 at pointsp1-p3, only the light ID and origins need to be sent to nodes 3610-3612.Each of the nodes may then stochastically sample the lightindependently. The random seed may be generated by the receiving node.Similarly, for primary ray hit points, ambient occlusion (AO) and softshadow sampling can be computed on nodes 3610-3612 without waiting forthe original points for successive frames. Additionally, if it is knownthat a set of rays will go to the same point light source, instructionsmay be sent identifying the light source to the receiving node whichwill apply it to the set of rays. As another example, if there are Nambient occlusion rays transmitted a single point, a command may be sentto generate N samples from this point.

Various additional techniques may be applied for lossy compression. Forexample, a quantization factor may be employed to quantize allcoordinate values associated with the BVH, primitives, and rays. Inaddition, 32-bit floating point values used for data such as BVH nodesand primitives may be converted into 8-bit integer values. In anexemplary implementation, the bounds of ray packets are stored in infull precision but individual ray points P1-P3 are transmitted asindexed offsets to the bounds. Similarly, a plurality of localcoordinate systems may be generated which use 8-bit integer values aslocal coordinates. The location of the origin of each of these localcoordinate systems may be encoded using the full precision (e.g., 32-bitfloating point) values, effectively connecting the global and localcoordinate systems.

The following is an example of lossless compression. An example of a Raydata format used internally in a ray tracing program is as follows:

  struct Ray {  uint32 pixId;  uint32 materialID;  uint32 instanceID; uint64 primitiveID;  uint32 geometryID;  uint32 lightID;  floatorigin[3];  float direction[3];  float t0;  float t;  float time;  floatnormal[3]; //used for geometry intersections  float u;  float v;  floatwavelength;  float phase; //Interferometry  float refractedOffset;//Schlieren-esque  float amplitude;  float weight; };

Instead of sending the raw data for each and every node generated, thisdata can be compressed by grouping values and by creating implicit raysusing applicable metadata where possible.

Bundling and Grouping Ray Data

Flags may be used for common data or masks with modifiers.

  struct RayPacket   {  uint32 size;  uint32 flags;  list<Ray> rays; }

For example:

-   -   RayPacket.rays=ray_1 to ray_256

Origins are all Shared

All ray data is packed, except only a single origin is stored across allrays. RayPacket.flags is set for RAYPACKET_COMMON_ORIGIN. When RayPacketis unpacked when received, origins are filled in from the single originvalue.

Origins are Shared Only Among Some Rays

All ray data is packed, except for rays that share origins. For eachgroup of unique shared origins, an operator is packed on that identifiesthe operation (shared origins), stores the origin, and masks which raysshare the information. Such an operation can be done on any sharedvalues among nodes such as material IDs, primitive IDs, origin,direction, normals, etc.

  struct RayOperation   {  uint8 operationID;  void* value;  uint64mask; }

Sending Implicit Rays

Often times, ray data can be derived on the receiving end with minimalmeta information used to generate it. A very common example isgenerating multiple secondary rays to stochastically sample an area.Instead of the sender generating a secondary ray, sending it, and thereceiver operating on it, the sender can send a command that a ray needsto be generated with any dependent information, and the ray is generatedon the receiving end. In the case where the ray needs to be firstgenerated by the sender to determine which receiver to send it to, theray is generated and the random seed can be sent to regenerate the exactsame ray.

For example, to sample a hit point with 64 shadow rays sampling an arealight source, all 64 rays intersect with regions from the same computeN4. A RayPacket with common origin and normal is created. More datacould be sent if one wished the receiver to shade the resulting pixelcontribution, but for this example let us assume we wish to only returnwhether a ray hits another nodes data. A RayOperation is created for agenerate shadow ray operation, and is assigned the value of the lightIDto be sampled and the random number seed. When N4 receives the raypacket, it generates the fully filled Ray data by filling in the sharedorigin data to all rays and setting the direction based on the lightIDstochastically sampled with the random number seed to generate the samerays that the original sender generated. When the results are returned,only binary results for every ray need be returned, which can be handedby a mask over the rays.

Sending the original 64 rays in this example would have used 104Bytes*64 rays=6656 Bytes. If the returning rays were sent in their rawform as well, than this is also doubled to 13312 Bytes. Using losslesscompression with only sending the common ray origin, normal, and raygeneration operation with seed and ID, only 29 Bytes are sent with 8Bytes returned for the was intersected mask. This results in a datacompression rate that needs to be sent over the network of ˜360:1. Thisdoes not include overhead to process the message itself, which wouldneed to be identified in some way, but that is left up to theimplementation. Other operations may be done for recomputing ray originand directions from the pixelD for primary rays, recalculating pixelIDsbased on the ranges in the raypacket, and many other possibleimplementations for recomputation of values. Similar operations can beused for any single or group of rays sent, including shadows,reflections, refraction, ambient occlusion, intersections, volumeintersections, shading, bounced reflections in path tracing, etc.

FIG. 37 illustrates additional details for two ray tracing nodes3710-3711 which perform compression and decompression of ray tracingpackets. In particular, when a first ray tracing engine 3730 is ready totransmit data to a second ray tracing engine 3731, ray compressioncircuitry 3720 performs lossy and/or lossless compression of the raytracing data as described herein (e.g., converting 32-bit values to8-bit values, substituting raw data for instructions to reconstruct thedata, etc). The compressed ray packets 3701 are transmitted from networkinterface 3725 to network interface 3726 over a local network (e.g., a10 Gb/s, 100 Gb/s Ethernet network). Ray decompression circuitry thendecompresses the ray packets when appropriate. For example, it mayexecute commands to reconstruct the ray tracing data (e.g., using arandom seed to perform random sampling for lighting operations). Raytracing engine 3731 then uses the received data to perform ray tracingoperations.

In the reverse direction, ray compression circuitry 3741 compresses raydata, network interface 3726 transmits the compressed ray data over thenetwork (e.g., using the techniques described herein), ray decompressioncircuitry 3740 decompresses the ray data when necessary and ray tracingengine 3730 uses the data in ray tracing operations. Althoughillustrated as a separate unit in FIG. 37 , ray decompression circuitry3740-3741 may be integrated within ray tracing engines 3730-3731,respectively. For example, to the extent the compressed ray datacomprises commands to reconstruct the ray data, these commands may beexecuted by each respective ray tracing engine 3730-3731.

As illustrated in FIG. 38 , ray compression circuitry 3720 may includelossy compression circuitry 3801 for performing the lossy compressiontechniques described herein (e.g., converting 32-bit floating pointcoordinates to 8-bit integer coordinates) and lossless compressioncircuitry 3803 for performing the lossless compression techniques (e.g.,transmitting commands and data to allow ray recompression circuitry 3821to reconstruct the data). Ray decompression circuitry 3721 includeslossy decompression circuitry 3802 and lossless decompression circuitry3804 for performing lossless decompression.

Another exemplary method is illustrated in FIG. 39 . The method may beimplemented on the ray tracing architectures or other architecturesdescribed herein but is not limited to any particular architecture.

At 3900, ray data is received which will be transmitted from a first raytracing node to a second ray tracing node. At 3901, lossy compressioncircuitry performs lossy compression on first ray tracing data and, at3902, lossless compression circuitry performs lossless compression onsecond ray tracing data. At 3903, the compressed ray racing data istransmitted to a second ray tracing node. At 3904, lossy/losslessdecompression circuitry performs lossy/lossless decompression of the raytracing data and, at 3905, the second ray tracing node performs raytracing operations sing the decompressed data.

Graphics Processor with Hardware Accelerated Hybrid Ray Tracing

A hybrid rendering pipeline which performs rasterization on graphicscores 3130 and ray tracing operations on the ray tracing cores 3150,graphics cores 3130, and/or CPU 3199 cores, is presented next. Forexample, rasterization and depth testing may be performed on thegraphics cores 3130 in place of the primary ray casting stage. The raytracing cores 3150 may then generate secondary rays for ray reflections,refractions, and shadows. In addition, certain regions of a scene inwhich the ray tracing cores 3150 will perform ray tracing operations(e.g., based on material property thresholds such as high reflectivitylevels) will be selected while other regions of the scene will berendered with rasterization on the graphics cores 3130. This hybridimplementation may be used for real-time ray tracing applications—wherelatency is a critical issue.

The ray traversal architecture described below may, for example, performprogrammable shading and control of ray traversal using existing singleinstruction multiple data (SIMD) and/or single instruction multiplethread (SIMT) graphics processors while accelerating critical functions,such as BVH traversal and/or intersections, using dedicated hardware.SIMD occupancy for incoherent paths may be improved by regroupingspawned shaders at specific points during traversal and before shading.This is achieved using dedicated hardware that sorts shadersdynamically, on-chip. Recursion is managed by splitting a function intocontinuations that execute upon returning and regrouping continuationsbefore execution for improved SIMD occupancy.

Programmable control of ray traversal/intersection is achieved bydecomposing traversal functionality into an inner traversal that can beimplemented as fixed function hardware and an outer traversal thatexecutes on GPU processors and enables programmable control through userdefined traversal shaders. The cost of transferring the traversalcontext between hardware and software is reduced by conservativelytruncating the inner traversal state during the transition between innerand outer traversal.

Programmable control of ray tracing can be expressed through thedifferent shader types listed in Table A below. There can be multipleshaders for each type. For example each material can have a differenthit shader.

TABLE A Shader Type Functionality Primary Launching primary rays HitBidirectional reflectance distribution function (BRDF) sampling,launching secondary rays Any Hit Computing transmittance for alphatextured geometry Miss Computing radiance from a light sourceIntersection Intersecting custom shapes Traversal Instance selection andtransformation Callable A general-purpose function

Recursive ray tracing may be initiated by an API function that commandsthe graphics processor to launch a set of primary shaders orintersection circuitry which can spawn ray-scene intersections forprimary rays. This in turn spawns other shaders such as traversal, hitshaders, or miss shaders. A shader that spawns a child shader can alsoreceive a return value from that child shader. Callable shaders aregeneral-purpose functions that can be directly spawned by another shaderand can also return values to the calling shader.

FIG. 40 illustrates a graphics processing architecture which includesshader execution circuitry 4000 and fixed function circuitry 4010. Thegeneral purpose execution hardware subsystem includes a plurality ofsingle instruction multiple data (SIMD) and/or single instructionsmultiple threads (SIMT) cores/execution units (EUs) 4001 (i.e., eachcore may comprise a plurality of execution units), one or more samplers4002, and a Level 1 (L1) cache 4003 or other form of local memory. Thefixed function hardware subsystem 4010 includes message unit 4004, ascheduler 4007, ray-BVH traversal/intersection circuitry 4005, sortingcircuitry 4008, and a local L1 cache 4006.

In operation, primary dispatcher 4009 dispatches a set of primary raysto the scheduler 4007, which schedules work to shaders executed on theSIMD/SIMT cores/EUs 4001. The SIMD cores/EUs 4001 may be ray tracingcores 3150 and/or graphics cores 3130 described above. Execution of theprimary shaders spawns additional work to be performed (e.g., to beexecuted by one or more child shaders and/or fixed function hardware).The message unit 4004 distributes work spawned by the SIMD cores/EUs4001 to the scheduler 4007, accessing the free stack pool as needed, thesorting circuitry 4008, or the ray-BVH intersection circuitry 4005. Ifthe additional work is sent to the scheduler 4007, it is scheduled forprocessing on the SIMD/SIMT cores/EUs 4001. Prior to scheduling, thesorting circuitry 4008 may sort the rays into groups or bins asdescribed herein (e.g., grouping rays with similar characteristics). Theray-BVH intersection circuitry 4005 performs intersection testing ofrays using BVH volumes. For example, the ray-BVH intersection circuitry4005 may compare ray coordinates with each level of the BVH to identifyvolumes which are intersected by the ray.

Shaders can be referenced using a shader record, a user-allocatedstructure that includes a pointer to the entry function, vendor-specificmetadata, and global arguments to the shader executed by the SIMDcores/EUs 4001. Each executing instance of a shader is associated with acall stack which may be used to store arguments passed between a parentshader and child shader. Call stacks may also store references to thecontinuation functions that are executed when a call returns.

FIG. 41 illustrates an example set of assigned stacks 4101 whichincludes a primary shader stack, a hit shader stack, a traversal shaderstack, a continuation function stack, and a ray-BVH intersection stack(which, as described, may be executed by fixed function hardware 4010).New shader invocations may implement new stacks from a free stack pool4102. The call stacks, e.g. stacks comprised by the set of assignedstacks, may be cached in a local L1 cache 4003, 4006 to reduce thelatency of accesses.

There may be a finite number of call stacks, each with a fixed maximumsize “Sstack” allocated in a contiguous region of memory. Therefore thebase address of a stack can be directly computed from a stack index(SID) as base address=SID*Sstack. Stack IDs may be allocated anddeallocated by the scheduler 4007 when scheduling work to the SIMDcores/EUs 4001.

The primary dispatcher 4009 may comprise a graphics processor commandprocessor which dispatches primary shaders in response to a dispatchcommand from the host (e.g., a CPU). The scheduler 4007 may receivethese dispatch requests and launches a primary shader on a SIMDprocessor thread if it can allocate a stack ID for each SIMD lane. StackIDs may be allocated from the free stack pool 4102 that is initializedat the beginning of the dispatch command.

An executing shader can spawn a child shader by sending a spawn messageto the messaging unit 4004. This command includes the stack IDsassociated with the shader and also includes a pointer to the childshader record for each active SIMD lane. A parent shader can only issuethis message once for an active lane. After sending spawn messages forall relevant lanes, the parent shader may terminate.

A shader executed on the SIMD cores/EUs 4001 can also spawnfixed-function tasks such as ray-BVH intersections using a spawn messagewith a shader record pointer reserved for the fixed-function hardware.As mentioned, the messaging unit 4004 sends spawned ray-BVH intersectionwork to the fixed-function ray-BVH intersection circuitry 4005 andcallable shaders directly to the sorting circuitry 4008. The sortingcircuitry may group the shaders by shader record pointer to derive aSIMD batch with similar characteristics. Accordingly, stack IDs fromdifferent parent shaders can be grouped by the sorting circuitry 4008 inthe same batch. The sorting circuitry 4008 sends grouped batches to thescheduler 4007 which accesses the shader record from graphics memory2511 or the last level cache (LLC) 4020 and launches the shader on aprocessor thread.

Continuations may be treated as callable shaders and may also bereferenced through shader records. When a child shader is spawned andreturns values to the parent shader, a pointer to the continuationshader record may be pushed on the call stack 4101. When a child shaderreturns, the continuation shader record may then be popped from the callstack 4101 and a continuation shader may be spawned. Optionally, spawnedcontinuations may go through the sorting unit similar to callableshaders and get launched on a processor thread.

As illustrated in FIG. 42 , the sorting circuitry 4008 groups spawnedtasks by shader record pointers 4201A, 4201B, 4201 n to create SIMDbatches for shading. The stack IDs or context IDs in a sorted batch canbe grouped from different dispatches and different input SIMD lanes. Agrouping circuitry 4210 may perform the sorting using a contentaddressable memory (CAM) structure 4201 comprising a plurality ofentries with each entry identified with a tag 4201. As mentioned, thetag 4201 may be a corresponding shader record pointer 4201A, 4201B, 4201n. The CAM structure 4201 may store a limited number of tags (e.g. 32,64, 128, etc) each associated with an incomplete SIMD batchcorresponding to a shader record pointer.

For an incoming spawn command, each SIMD lane has a corresponding stackID (shown as 16 context IDs 0-15 in each CAM entry) and a shader recordpointer 4201A-B, . . . n (acting as a tag value). The grouping circuitry4210 may compare the shader record pointer for each lane against thetags 4201 in the CAM structure 4201 to find a matching batch. If amatching batch is found, the stack ID/context ID may be added to thebatch. Otherwise a new entry with a new shader record pointer tag may becreated, possibly evicting an older entry with an incomplete batch.

An executing shader can deallocate the call stack when it is empty bysending a deallocate message to the message unit. The deallocate messageis relayed to the scheduler which returns stack IDs/context IDs foractive SIMD lanes to the free pool.

A hybrid approach for ray traversal operations, using a combination offixed-function ray traversal and software ray traversal, is presented.Consequently, it provides the flexibility of software traversal whilemaintaining the efficiency of fixed-function traversal. FIG. 43 shows anacceleration structure which may be used for hybrid traversal, which isa two-level tree with a single top level BVH 4300 and several bottomlevel BVHs 4301 and 4302. Graphical elements are shown to the right toindicate inner traversal paths 4303, outer traversal paths 4304,traversal nodes 4305, leaf nodes with triangles 4306, and leaf nodeswith custom primitives 4307.

The leaf nodes with triangles 4306 in the top level BVH 4300 canreference triangles, intersection shader records for custom primitivesor traversal shader records. The leaf nodes with triangles 4306 of thebottom level BVHs 4301-4302 can only reference triangles andintersection shader records for custom primitives. The type of referenceis encoded within the leaf node 4306. Inner traversal 4303 refers totraversal within each BVH 4300-4302. Inner traversal operations comprisecomputation of ray-BVH intersections and traversal across the BVHstructures 4300-4302 is known as outer traversal. Inner traversaloperations can be implemented efficiently in fixed function hardwarewhile outer traversal operations can be performed with acceptableperformance with programmable shaders. Consequently, inner traversaloperations may be performed using fixed-function circuitry 4010 andouter traversal operations may be performed using the shader executioncircuitry 4000 including SIMD/SIMT cores/EUs 4001 for executingprogrammable shaders.

Note that the SIMD/SIMT cores/EUs 4001 are sometimes simply referred toherein as “cores,” “SIMD cores,” “EUs,” or “SIMD processors” forsimplicity. Similarly, the ray-BVH traversal/intersection circuitry 4005is sometimes simply referred to as a “traversal unit,”“traversal/intersection unit” or “traversal/intersection circuitry.”When an alternate term is used, the particular name used to designatethe respective circuitry/logic does not alter the underlying functionswhich the circuitry/logic performs, as described herein.

Moreover, while illustrated as a single component in FIG. 40 forpurposes of explanation, the traversal/intersection unit 4005 maycomprise a distinct traversal unit and a separate intersection unit,each of which may be implemented in circuitry and/or logic as describedherein.

When a ray intersects a traversal node during an inner traversal, atraversal shader may be spawned. The sorting circuitry 4008 may groupthese shaders by shader record pointers 4201A-B, n to create a SIMDbatch which is launched by the scheduler 4007 for SIMD execution on thegraphics SIMD cores/EUs 4001. Traversal shaders can modify traversal inseveral ways, enabling a wide range of applications. For example, thetraversal shader can select a BVH at a coarser level of detail (LOD) ortransform the ray to enable rigid body transformations. The traversalshader may then spawn inner traversal for the selected BVH.

Inner traversal computes ray-BVH intersections by traversing the BVH andcomputing ray-box and ray-triangle intersections. Inner traversal isspawned in the same manner as shaders by sending a message to themessaging circuitry 4004 which relays the corresponding spawn message tothe ray-BVH intersection circuitry 4005 which computes ray-BVHintersections.

The stack for inner traversal may be stored locally in thefixed-function circuitry 4010 (e.g., within the L1 cache 4006). When aray intersects a leaf node corresponding to a traversal shader or anintersection shader, inner traversal may be terminated and the innerstack truncated. The truncated stack along with a pointer to the ray andBVH may be written to memory at a location specified by the callingshader and then the corresponding traversal shader or intersectionshader may be spawned. If the ray intersects any triangles during innertraversal, the corresponding hit information may be provided as inputarguments to these shaders as shown in the below code. These spawnedshaders may be grouped by the sorting circuitry 4008 to create SIMDbatches for execution.

  struct HitInfo {  float barycentrics[2];  float tmax;  boolinnerTravComplete;  uint primID;  uint geomID;  ShaderRecord*leafShaderRecord; }

Truncating the inner traversal stack reduces the cost of spilling it tomemory. The approach described in Restart Trail for Stackless BVHTraversal, High Performance Graphics (2010), pp. 107-111, to truncatethe stack to a small number of entries at the top of the stack, a 42-bitrestart trail and a 6-bit depth value may be applied. The restart trailindicates branches that have already been taken inside the BVH and thedepth value indicates the depth of traversal corresponding to the laststack entry. This is sufficient information to resume inner traversal ata later time.

Inner traversal is complete when the inner stack is empty and there nomore BVH nodes to test. In this case an outer stack handler is spawnedthat pops the top of the outer stack and resumes traversal if the outerstack is not empty.

Outer traversal may execute the main traversal state machine and may beimplemented in program code executed by the shader execution circuitry4000. It may spawn an inner traversal query under the followingconditions: (1) when a new ray is spawned by a hit shader or a primaryshader; (2) when a traversal shader selects a BVH for traversal; and (3)when an outer stack handler resumes inner traversal for a BVH.

As illustrated in FIG. 44 , before inner traversal is spawned, space isallocated on the call stack 4405 for the fixed-function circuitry 4010to store the truncated inner stack 4410. Offsets 4403-4404 to the top ofthe call stack and the inner stack are maintained in the traversal state4400 which is also stored in memory 2511. The traversal state 4400 alsoincludes the ray in world space 4401 and object space 4402 as well ashit information for the closest intersecting primitive.

The traversal shader, intersection shader and outer stack handler areall spawned by the ray-BVH intersection circuitry 4005. The traversalshader allocates on the call stack 4405 before initiating a new innertraversal for the second level BVH. The outer stack handler is a shaderthat is responsible for updating the hit information and resuming anypending inner traversal tasks. The outer stack handler is alsoresponsible for spawning hit or miss shaders when traversal is complete.Traversal is complete when there are no pending inner traversal queriesto spawn. When traversal is complete and an intersection is found, a hitshader is spawned; otherwise a miss shader is spawned.

While the hybrid traversal scheme described above uses a two-level BVHhierarchy, an arbitrary number of BVH levels with a corresponding changein the outer traversal implementation may also be implemented.

In addition, while fixed function circuitry 4010 is described above forperforming ray-BVH intersections, other system components may also beimplemented in fixed function circuitry. For example, the outer stackhandler described above may be an internal (not user visible) shaderthat could potentially be implemented in the fixed function BVHtraversal/intersection circuitry 4005. This implementation may be usedto reduce the number of dispatched shader stages and round trips betweenthe fixed function intersection hardware 4005 and the processor.

The examples described herein enable programmable shading and raytraversal control using user-defined functions that can execute withgreater SIMD efficiency on existing and future GPU processors.Programmable control of ray traversal enables several important featuressuch as procedural instancing, stochastic level-of-detail selection,custom primitive intersection and lazy BVH updates.

A programmable, multiple instruction multiple data (MIMD) ray tracingarchitecture which supports speculative execution of hit andintersection shaders is also provided. In particular, the architecturefocuses on reducing the scheduling and communication overhead betweenthe programmable SIMD/SIMT cores/execution units 4001 described abovewith respect to FIG. 40 and fixed-function MIMD traversal/intersectionunits 4005 in a hybrid ray tracing architecture. Multiple speculativeexecution schemes of hit and intersection shaders are described belowthat can be dispatched in a single batch from the traversal hardware,avoiding several traversal and shading round trips. A dedicatedcircuitry to implement these techniques may be used.

The embodiments of the invention are particularly beneficial inuse-cases where the execution of multiple hit or intersection shaders isdesired from a ray traversal query that would impose significantoverhead when implemented without dedicated hardware support. Theseinclude, but are not limited to nearest k-hit query (launch a hit shaderfor the k closest intersections) and multiple programmable intersectionshaders.

The techniques described here may be implemented as extensions to thearchitecture illustrated in FIG. 40 (and described with respect to FIGS.40-44 ). In particular, the present embodiments of the invention buildon this architecture with enhancements to improve the performance of theabove-mentioned use-cases.

A performance limitation of hybrid ray tracing traversal architecturesis the overhead of launching traversal queries from the execution unitsand the overhead of invoking programmable shaders from the ray tracinghardware. When multiple hit or intersection shaders are invoked duringthe traversal of the same ray, this overhead generates “executionroundtrips” between the programmable cores 4001 andtraversal/intersection unit 4005. This also places additional pressureto the sorting unit 4008 which needs to extract SIMD/SIMT coherence fromthe individual shader invocations.

Several aspects of ray tracing require programmable control which can beexpressed through the different shader types listed in TABLE A above(i.e., Primary, Hit, Any Hit, Miss, Intersection, Traversal, andCallable). There can be multiple shaders for each type. For example eachmaterial can have a different hit shader. Some of these shader types aredefined in the current Microsoft® Ray Tracing API.

As a brief review, recursive ray tracing is initiated by an API functionthat commands the GPU to launch a set of primary shaders which can spawnray-scene intersections (implemented in hardware and/or software) forprimary rays. This in turn can spawn other shaders such as traversal,hit or miss shaders. A shader that spawns a child shader can alsoreceive a return value from that shader. Callable shaders aregeneral-purpose functions that can be directly spawned by another shaderand can also return values to the calling shader.

Ray traversal computes ray-scene intersections by traversing andintersecting nodes in a bounding volume hierarchy (BVH). Recent researchhas shown that the efficiency of computing ray-scene intersections canbe improved by over an order of magnitude using techniques that arebetter suited to fixed-function hardware such as reduced-precisionarithmetic, BVH compression, per-ray state machines, dedicatedintersection pipelines and custom caches.

The architecture shown in FIG. 40 comprises such a system where an arrayof SIMD/SIMT cores/execution units 4001 interact with a fixed functionray tracing/intersection unit 4005 to perform programmable ray tracing.Programmable shaders are mapped to SIMD/SIMT threads on the executionunits/cores 4001, where SIMD/SIMT utilization, execution, and datacoherence are critical for optimal performance. Ray queries often breakup coherence for various reasons such as:

-   -   Traversal divergence: The duration of the BVH traversal varies        highly    -   among rays favoring asynchronous ray processing.    -   Execution divergence: Rays spawned from different lanes of the        same SIMD/SIMT thread may result in different shader        invocations.    -   Data access divergence: Rays hitting different surfaces sample        different BVH nodes and primitives and shaders access different        textures, for example. A variety of other scenarios may cause        data access divergence.

The SIMD/SIMT cores/execution units 4001 may be variants ofcores/execution units described herein including graphics core(s)415A-415B, shader cores 1355A-N, graphics cores 3130, graphics executionunit 608, execution units 852A-B, or any other cores/execution unitsdescribed herein. The SIMD/SIMT cores/execution units 4001 may be usedin place of the graphics core(s) 415A-415B, shader cores 1355A-N,graphics cores 3130, graphics execution unit 608, execution units852A-B, or any other cores/execution units described herein. Therefore,the disclosure of any features in combination with the graphics core(s)415A-415B, shader cores 1355A-N, graphics cores 3130, graphics executionunit 608, execution units 852A-B, or any other cores/execution unitsdescribed herein also discloses a corresponding combination with theSIMD/SIMT cores/execution units 4001 of FIG. 40 , but is not limited tosuch.

The fixed-function ray tracing/intersection unit 4005 may overcome thefirst two challenges by processing each ray individually andout-of-order. That, however, breaks up SIMD/SIMT groups. The sortingunit 4008 is hence responsible for forming new, coherent SIMD/SIMTgroups of shader invocations to be dispatched to the execution unitsagain.

It is easy to see the benefits of such an architecture compared to apure software-based ray tracing implementation directly on the SIMD/SIMTprocessors. However, there is an overhead associated with the messagingbetween the SIMD/SIMT cores/execution units 4001 (sometimes simplyreferred to herein as SIMD/SIMT processors or cores/EUs) and the MIMDtraversal/intersection unit 4005. Furthermore, the sorting unit 4008 maynot extract perfect SIMD/SIMT utilization from incoherent shader calls.

Use-cases can be identified where shader invocations can be particularlyfrequent during traversal. Enhancements are described for hybrid MIMDray tracing processors to significantly reduce the overhead ofcommunication between the cores/EUs 4001 and traversal/intersectionunits 4005. This may be particularly beneficial when finding thek-closest intersections and implementation of programmable intersectionshaders. Note, however, that the techniques described here are notlimited to any particular processing scenario.

A summary of the high-level costs of the ray tracing context switchbetween the cores/EUs 4001 and fixed function traversal/intersectionunit 4005 is provided below. Most of the performance overhead is causedby these two context switches every time when the shader invocation isnecessary during single-ray traversal.

Each SIMD/SIMT lane that launches a ray generates a spawn message to thetraversal/intersection unit 4005 associated with a BVH to traverse. Thedata (ray traversal context) is relayed to the traversal/intersectionunit 4005 via the spawn message and (cached) memory. When thetraversal/intersection unit 4005 is ready to assign a new hardwarethread to the spawn message it loads the traversal state and performstraversal on the BVH. There is also a setup cost that needs to beperformed before first traversal step on the BVH.

FIG. 45 illustrates an operational flow of a programmable ray tracingpipeline. The shaded elements including traversal 4502 and intersection4503 may be implemented in fixed function circuitry while the remainingelements may be implemented with programmable cores/execution units.

A primary ray shader 4501 sends work to the traversal circuitry at 4502which traverses the current ray(s) through the BVH (or otheracceleration structure). When a leaf node is reached, the traversalcircuitry calls the intersection circuitry at 4503 which, uponidentifying a ray-triangle intersection, invokes an any hit shader at4504 (which may provide results back to the traversal circuitry asindicated).

Alternatively, the traversal may be terminated prior to reaching a leafnode and a closest hit shader invoked at 4507 (if a hit was recorded) ora miss shader at 4506 (in the event of a miss).

As indicated at 4505, an intersection shader may be invoked if thetraversal circuitry reaches a custom primitive leaf node. A customprimitive may be any non-triangle primitive such as a polygon or apolyhedra (e.g., tetrahedrons, voxels, hexahedrons, wedges, pyramids, orother “unstructured” volume). The intersection shader 4505 identifiesany intersections between the ray and custom primitive to the any hitshader 4504 which implements any hit processing.

When hardware traversal 4502 reaches a programmable stage, thetraversal/intersection unit 4005 may generate a shader dispatch messageto a relevant shader 4505-4507, which corresponds to a single SIMD laneof the execution unit(s) used to execute the shader. Since dispatchesoccur in an arbitrary order of rays, and they are divergent in theprograms called, the sorting unit 4008 may accumulate multiple dispatchcalls to extract coherent SIMD batches. The updated traversal state andthe optional shader arguments may be written into memory 2511 by thetraversal/intersection unit 4005.

In the k-nearest intersection problem, a closest hit shader 4507 isexecuted for the first k intersections. In the conventional way thiswould mean ending ray traversal upon finding the closest intersection,invoking a hit-shader, and spawning a new ray from the hit shader tofind the next closest intersection (with the ray origin offset, so thesame intersection will not occur again). It is easy to see that thisimplementation would require k ray spawns for a single ray. Anotherimplementation operates with any-hit shaders 4504, invoked for allintersections and maintaining a global list of nearest intersections,using an insertion sort operation. The main problem with this approachis that there is no upper bound of any-hit shader invocations.

As mentioned, an intersection shader 4505 may be invoked on non-triangle(custom) primitives. Depending on the result of the intersection testand the traversal state (pending node and primitive intersections), thetraversal of the same ray may continue after the execution of theintersection shader 4505. Therefore finding the closest hit may requireseveral roundtrips to the execution unit.

A focus can also be put on the reduction of SIMD-MIMD context switchesfor intersection shaders 4505 and hit shaders 4504, 4507 through changesto the traversal hardware and the shader scheduling model. First, theray traversal circuitry 4005 defers shader invocations by accumulatingmultiple potential invocations and dispatching them in a larger batch.In addition, certain invocations that turn out to be unnecessary may beculled at this stage. Furthermore, the shader scheduler 4007 mayaggregate multiple shader invocations from the same traversal contextinto a single SIMD batch, which results in a single ray spawn message.In one exemplary implementation, the traversal hardware 4005 suspendsthe traversal thread and waits for the results of multiple shaderinvocations. This mode of operation is referred to herein as“speculative” shader execution because it allows the dispatch ofmultiple shaders, some of which may not be called when using sequentialinvocations.

FIG. 46A illustrates an example in which the traversal operationencounters multiple custom primitives 4650 in a subtree and FIG. 46Billustrates how this can be resolved with three intersection dispatchcycles C1-C3. In particular, the scheduler 4007 may require three cyclesto submit the work to the SIMD processor 4001 and the traversalcircuitry 4005 requires three cycles to provide the results to thesorting unit 4008. The traversal state 4601 required by the traversalcircuitry 4005 may be stored in a memory such as a local cache (e.g., anL1 cache and/or L2 cache).

A. Deferred Ray Tracing Shader Invocations

The manner in which the hardware traversal state 4601 is managed toallow the accumulation of multiple potential intersection or hitinvocations in a list can also be modified. At a given time duringtraversal each entry in the list may be used to generate a shaderinvocation. For example, the k-nearest intersection points can beaccumulated on the traversal hardware 4005 and/or in the traversal state4601 in memory, and hit shaders can be invoked for each element if thetraversal is complete. For hit shaders, multiple potential intersectionsmay be accumulated for a subtree in the BVH.

For the nearest-k use case the benefit of this approach is that insteadof k−1 roundtrips to the SIMD core/EU 4001 and k−1 new ray spawnmessages, all hit shaders are invoked from the same traversal threadduring a single traversal operation on the traversal circuitry 4005. Achallenge for potential implementations is that it is not trivial toguarantee the execution order of hit shaders (the standard “roundtrip”approach guarantees that the hit shader of the closest intersection isexecuted first, etc.). This may be addressed by either thesynchronization of the hit shaders or the relaxation of the ordering.

For the intersection shader use case the traversal circuitry 4005 doesnot know in advance whether a given shader would return a positiveintersection test. However, it is possible to speculatively executemultiple intersection shaders and if at least one returns a positive hitresult, it is merged into the global nearest hit. Specificimplementations need to find an optimal number of deferred intersectiontests to reduce the number of dispatch calls but avoid calling too manyredundant intersection shaders.

B. Aggregate Shader Invocations from the Traversal Circuitry

When dispatching multiple shaders from the same ray spawn on thetraversal circuitry 4005, branches in the flow of the ray traversalalgorithm may be created. This may be problematic for intersectionshaders because the rest of the BVH traversal depend on the result ofall dispatched intersection tests. This means that a synchronizationoperation is necessary to wait for the result of the shader invocations,which can be challenging on asynchronous hardware.

Two points of merging the results of the shader calls may be: the SIMDprocessor 4001, and the traversal circuitry 4005. With respect to theSIMD processor 4001, multiple shaders can synchronize and aggregatetheir results using standard programming models. One relatively simpleway to do this is to use global atomics and aggregate results in ashared data structure in memory, where intersection results of multipleshaders could be stored. Then the last shader can resolve the datastructure and call back the traversal circuitry 4005 to continue thetraversal.

A more efficient approach may also be implemented which limits theexecution of multiple shader invocations to lanes of the same SIMDthread on the SIMD processor 4001. The intersection tests are thenlocally reduced using SIMD/SIMT reduction operations (rather thanrelying on global atomics). This implementation may rely on newcircuitry within the sorting unit 4008 to let a small batch of shaderinvocations stay in the same SIMD batch.

The execution of the traversal thread may further be suspended on thetraversal circuitry 4005. Using the conventional execution model, when ashader is dispatched during traversal, the traversal thread isterminated and the ray traversal state is saved to memory to allow theexecution of other ray spawn commands while the execution units 4001process the shaders. If the traversal thread is merely suspended, thetraversal state does not need to be stored and can wait for each shaderresult separately. This implementation may include circuitry to avoiddeadlocks and provide sufficient hardware utilization.

FIGS. 47-48 illustrate examples of a deferred model which invokes asingle shader invocation on the SIMD cores/execution units 4001 withthree shaders 4701. When preserved, all intersection tests are evaluatedwithin the same SIMD/SIMT group. Consequently, the nearest intersectioncan also be computed on the programmable cores/execution units 4001.

As mentioned, all or a portion of the shader aggregation and/or deferralmay be performed by the traversal/intersection circuitry 4005 and/or thecore/EU scheduler 4007. FIG. 47 illustrates how shaderdeferral/aggregator circuitry 4706 within the scheduler 4007 can deferscheduling of shaders associated with a particular SIMD/SIMT thread/laneuntil a specified triggering event has occurred. Upon detecting thetriggering event, the scheduler 4007 dispatches the multiple aggregatedshaders in a single SIMD/SIMT batch to the cores/EUs 4001.

FIG. 48 illustrates how shader deferral/aggregator circuitry 4805 withinthe traversal/intersection circuitry 4005 can defer scheduling ofshaders associated with a particular SIMD thread/lane until a specifiedtriggering event has occurred. Upon detecting the triggering event, thetraversal/intersection circuitry 4005 submits the aggregated shaders tothe sorting unit 4008 in a single SIMD/SIMT batch.

Note, however, that the shader deferral and aggregation techniques maybe implemented within various other components such as the sorting unit4008 or may be distributed across multiple components. For example, thetraversal/intersection circuitry 4005 may perform a first set of shaderaggregation operations and the scheduler 4007 may perform a second setof shader aggregation operations to ensure that shaders for a SIMDthread are scheduled efficiently on the cores/EUs 4001.

The “triggering event” to cause the aggregated shaders to be dispatchedto the cores/EUs may be a processing event such as a particular numberof accumulated shaders or a minimum latency associated with a particularthread. Alternatively, or in addition, the triggering event may be atemporal event such as a certain duration from the deferral of the firstshader or a particular number of processor cycles. Other variables suchas the current workload on the cores/EUs 4001 and thetraversal/intersection unit 4005 may also be evaluated by the scheduler4007 to determine when to dispatch the SIMD/SIMT batch of shaders.

Different embodiments of the invention may be implemented usingdifferent combinations of the above approaches, based on the particularsystem architecture being used and the requirements of the application.

Ray Tracing Instructions

The ray tracing instructions described below are included in aninstruction set architecture (ISA) supported the CPU 3199 and/or GPU3105. If executed by the CPU, the single instruction multiple data(SIMD) instructions may utilize vector/packed source and destinationregisters to perform the described operations and may be decoded andexecuted by a CPU core. If executed by a GPU 3105, the instructions maybe executed by graphics cores 3130. For example, any of the executionunits (EUs) 4001 described above may execute the instructions.Alternatively, or in addition, the instructions may be executed byexecution circuitry on the ray tracing cores 3150 and/or tensor corestensor cores 3140.

FIG. 49 illustrates an architecture for executing the ray tracinginstructions described below. The illustrated architecture may beintegrated within one or more of the cores 3130, 3140, 3150 describedabove (see, e.g., FIG. 31 and associated text) of may be included in adifferent processor architecture.

In operation, an instruction fetch unit 4903 fetches ray tracinginstructions 4900 from memory 3198 and a decoder 4995 decodes theinstructions. In one implementation the decoder 4995 decodesinstructions to generate executable operations (e.g., microoperations oruops in a microcoded core). Alternatively, some or all of the raytracing instructions 4900 may be executed without decoding and, as sucha decoder 4904 is not required.

In either implementation, a scheduler/dispatcher 4905 schedules anddispatches the instructions (or operations) across a set of functionalunits (FUs) 4910-4912. The illustrated implementation includes a vectorFU 4910 for executing single instruction multiple data (SIMD)instructions which operate concurrently on multiple packed data elementsstored in vector registers 4915 and a scalar FU 4911 for operating onscalar values stored in one or more scalar registers 4916. An optionalray tracing FU 4912 may operate on packed data values stored in thevector registers 4915 and/or scalar values stored in the scalarregisters 4916. In an implementation without a dedicated FU 4912, thevector FU 4910 and possibly the scalar FU 4911 may perform the raytracing instructions described below.

The various FUs 4910-4912 access ray tracing data 4902 (e.g.,traversal/intersection data) needed to execute the ray tracinginstructions 4900 from the vector registers 4915, scalar register 4916and/or the local cache subsystem 4908 (e.g., a L1 cache). The FUs4910-4912 may also perform accesses to memory 3198 via load and storeoperations, and the cache subsystem 4908 may operate independently tocache the data locally.

While the ray tracing instructions may be used to increase performancefor ray traversal/intersection and BVH builds, they may also beapplicable to other areas such as high performance computing (HPC) andgeneral purpose GPU (GPGPU) implementations.

In the below descriptions, the term double word is sometimes abbreviateddw and unsigned byte is abbreviated ub. In addition, the source anddestination registers referred to below (e.g., src0, src1, dest, etc)may refer to vector registers 4915 or in some cases a combination ofvector registers 4915 and scalar registers 4916. Typically, if a sourceor destination value used by an instruction includes packed dataelements (e.g., where a source or destination stores N data elements),vector registers 4915 are used. Other values may use scalar registers4916 or vector registers 4915.

Dequantize

One example of the Dequantize instruction “dequantizes” previouslyquantized values. By way of example, in a ray tracing implementation,certain BVH subtrees may be quantized to reduce storage and bandwidthrequirements. The dequantize instruction may take the form dequantizedest src0 src1 src2 where source register src0 stores N unsigned bytes,source register src1 stores 1 unsigned byte, source register src2 stores1 floating point value, and destination register dest stores N floatingpoint values. All of these registers may be vector registers 4915.Alternatively, src0 and dest may be vector registers 4915 and src 1 andsrc2 may be scalar registers 4916.

The following code sequence defines one particular implementation of thedequantize instruction:

  for (int i = 0; i < SIMD_WIDTH) {  if (execMask[i]) {   dst[i] =src2[i] + Idexp(convert_to_float(src0[i]),src1);  } }

In this example, Idexp multiplies a double precision floating pointvalue by a specified integral power of two (i.e., Idexp(x,exp)=x*2^(exp)). In the above code, if the execution mask valueassociated with the current SIMD data element (execMask[i])) is set to1, then the SIMD data element at location i in src0 is converted to afloating point value and multiplied by the integral power of the valuein src1 (2^(src1 value)) and this value is added to the correspondingSIMD data element in src2.

Selective Min or Max

A selective min or max instruction may perform either a min or a maxoperation per lane (i.e., returning the minimum or maximum of a set ofvalues), as indicated by a bit in a bitmask. The bitmask may utilize thevector registers 4915, scalar registers 4916, or a separate set of maskregisters (not shown). The following code sequence defines oneparticular implementation of the min/max instruction: sel_min_max destsrc0 src1 src2, where src0 stores N doublewords, src1 stores Ndoublewords, src2 stores one doubleword, and the destination registerstores N doublewords.

The following code sequence defines one particular implementation of theselective min/max instruction:

  for (int i = 0; i < SIMD_WIDTH) {  if (execMask[i]) {  dst[i] = (1 <<i) & src2 ? min(src0[i],src1[i]) :  max(src0[i],src1[i]);  } }

In this example, the value of (1<<i) & src2 (a 1 left-shifted by i ANDedwith src2) is used to select either the minimum of the i^(th) dataelement in src0 and src1 or the maximum of the i^(th) data element insrc0 and src1. The operation is performed for the i^(th) data elementonly if the execution mask value associated with the current SIMD dataelement (execMask[i])) is set to 1.

Shuffle Index Instruction

A shuffle index instruction can copy any set of input lanes to theoutput lanes. For a SIMD width of 32, this instruction can be executedat a lower throughput. This instruction takes the form: shuffle_indexdest src0 src1 <optional flag>, where src0 stores N doublewords, src1stores N unsigned bytes (i.e., the index value), and dest stores Ndoublewords.

The following code sequence defines one particular implementation of theshuffle index instruction:

  for (int i = 0; i < SIMD_WIDTH) {  uint8_t srcLane = src1.index[i]; if (execMask[i]) {   bool invalidLane = srcLane < 0 | | srcLane >=SIMD_WIDTH | | !execMask[srcLaneMod];   if (FLAG) {    invalidLane | =flag[srcLaneMod];   }   if (invalidLane) {    dst[i] = src0[i];   }  else {    dst[i] = src0[srcLane];   }  } }

In the above code, the index in src1 identifies the current lane. If thei^(th) value in the execution mask is set to 1, then a check isperformed to ensure that the source lane is within the range of 0 to theSIMD width. If so, then flag is set (srcLaneMod) and data element i ofthe destination is set equal to data element i of src0. If the lane iswithin range (i.e., is valid), then the index value from src1 (srcLane0)is used as an index into src0 (dst[i]=src0[srcLane]).

Immediate Shuffle Up/Dn/XOR Instruction

An immediate shuffle instruction may shuffle input data elements/lanesbased on an immediate of the instruction. The immediate may specifyshifting the input lanes by 1, 2, 4, 8, or 16 positions, based on thevalue of the immediate. Optionally, an additional scalar source registercan be specified as a fill value. When the source lane index is invalid,the fill value (if provided) is stored to the data element location inthe destination. If no fill value is provided, the data element locationis set to all 0.

A flag register may be used as a source mask. If the flag bit for asource lane is set to 1, the source lane may be marked as invalid andthe instruction may proceed.

The following are examples of different implementations of the immediateshuffle instruction:

  shuffle_< up/dn/xor>_< 1/2/4/8/16> dest src0 <optional src1> <optionalflag> shuffle_< up/dn/xor>_< 1/2/4/8/16> dest src0 <optional src1><optional flag>

In this implementation, src0 stores N doublewords, src1 stores onedoubleword for the fill value (if present), and dest stores Ndoublewords comprising the result.

The following code sequence defines one particular implementation of theimmediate shuffle instruction:

  for (int i = 0; i < SIMD_WIDTH) {  int8_t srcLane;  switch(SHUFFLE_TYPE) {  case UP:   srcLane = i − SHIFT;  case DN :   srcLane =i + SHIFT;  case XOR:   srcLane = i {circumflex over ( )} SHIFT;  }  if(execMask[i]) {   bool invalidLane = srcLane < 0 | | srcLane >=SIMD_WIDTH | | !execMask[srcLane];   if (FLAG) {    invalidLane | =flag[srcLane];   }   if (invalidLane) {    if (SRC1)     dst[i] = src1;   else     dst[i] = 0;   }   else {    dst[i] = src0[srcLane];   }  } }

Here the input data elements/lanes are shifted by 1, 2, 4, 8, or 16positions, based on the value of the immediate. The register src1 is anadditional scalar source register which is used as a fill value which isstored to the data element location in the destination when the sourcelane index is invalid. If no fill value is provided and the source laneindex is invalid, the data element location in the destination is set to0s. The flag register (FLAG) is used as a source mask. If the flag bitfor a source lane is set to 1, the source lane is marked as invalid andthe instruction proceeds as described above.

Indirect Shuffle Up/Dn/XOR Instruction

The indirect shuffle instruction has a source operand (src1) thatcontrols the mapping from source lanes to destination lanes. Theindirect shuffle instruction may take the form:

-   -   shuffle_<up/dn/xor> dest src0 src1 <optional flag>        where src0 stores N doublewords, src1 stores 1 doubleword, and        dest stores N doublewords.

The following code sequence defines one particular implementation of theimmediate shuffle instruction:

  for (int i = 0; i < SIMD_WIDTH) {  int8_t srcLane; switch(SHUFFLE_TYPE) {  case UP:   srcLane = i − src1;  case DN:  srcLane = i + src1;  case XOR:   srcLane = i {circumflex over ( )}src1;  }  if (execMask[i]) {   bool invalidLane = srcLane < 0 | |srcLane >= SIMD_WIDTH | | !execMask[srcLane];   if (FLAG) {   invalidLane | = flag[srcLane];   }   if (invalidLane) {    dst[i] =0;   }   else {    dst[i] = src0[srcLane];   }  } }

Thus, the indirect shuffle instruction operates in a similar manner tothe immediate shuffle instruction described above, but the mapping ofsource lanes to destination lanes is controlled by the source registersrc1 rather than the immediate.

Cross Lane Min/Max Instruction

A cross lane minimum/maximum instruction may be supported for float andinteger data types. The cross lane minimum instruction may take the formlane_min dest src0 and the cross lane maximum instruction may take theform lane_max dest src0, where src0 stores N doublewords and dest stores1 doubleword.

By way of example, the following code sequence defines one particularimplementation of the cross lane minimum:

  dst = src[0]; for (int i = 1; i < SIMD_WIDTH) {  if (execMask[i]) {  dst = min(dst, src[i]);  } }In this example, the doubleword value in data element position i of thesource register is compared with the data element in the destinationregister and the minimum of the two values is copied to the destinationregister. The cross lane maximum instruction operates in substantiallythe same manner, the only difference being that the maximum of the dataelement in position i and the destination value is selected.

Cross Lane Min/Max Index Instruction

A cross lane minimum index instruction may take the form lane_min_indexdest src0 and the cross lane maximum index instruction may take the formlane_max_index dest src0, where src0 stores N doublewords and deststores 1 doubleword.

By way of example, the following code sequence defines one particularimplementation of the cross lane minimum index instruction:

  dst_index = 0; tmp = src[0] for (int i = 1; i < SIMD_WIDTH) {  if(src[i] < tmp && execMask[i])  {   tmp = src[i];   dst_index = i;  } }

In this example, the destination index is incremented from 0 to SIMDwidth, spanning the destination register. If the execution mask bit isset, then the data element at position i in the source register iscopied to a temporary storage location (tmp) and the destination indexis set to data element position i.

Cross Lane Sorting Network Instruction

A cross-lane sorting network instruction may sort all N input elementsusing an N-wide (stable) sorting network, either in ascending order(sortnet_min) or in descending order (sortnet_max). The min/max versionsof the instruction may take the forms sortnet_min dest src0 andsortnet_max dest src0, respectively. In one implementation, src0 anddest store N doublewords. The min/max sorting is performed on the Ndoublewords of src0, and the ascending ordered elements (for min) ordescending ordered elements (for max) are stored in dest in theirrespective sorted orders. One example of a code sequence defining theinstruction is: dst=apply_N_wide_sorting_network_min/max(src0).

Cross Lane Sorting Network Index Instruction

A cross-lane sorting network index instruction may sort all N inputelements using an N-wide (stable) sorting network but returns thepermute index, either in ascending order (sortnet_min) or in descendingorder (sortnet_max). The min/max versions of the instruction may takethe forms sortnet_min_index dest src0 and sortnet_max_index dest src0where src0 and dest each store N doublewords. One example of a codesequence defining the instruction isdst=apply_N_wide_sorting_network_min/max_index(src0).

A method for executing any of the above instructions is illustrated inFIG. 50 . The method may be implemented on the specific processorarchitectures described above, but is not limited to any particularprocessor or system architecture.

At 5001 instructions of a primary graphics thread are executed onprocessor cores. This may include, for example, any of the coresdescribed above (e.g., graphics cores 3130). When ray tracing work isreached within the primary graphics thread, determined at 5002, the raytracing instructions are offloaded to the ray tracing executioncircuitry which may be in the form of a functional unit (FU) such asdescribed above with respect to FIG. 49 or which may be in a dedicatedray tracing core 3150 as described with respect to FIG. 31 .

At 5003, the ray tracing instructions are decoded are fetched frommemory and, at 5005, the instructions are decoded into executableoperations (e.g., in an embodiment which requires a decoder). At 5004the ray tracing instructions are scheduled and dispatched for executionby ray tracing circuitry. At 5005 the ray tracing instructions areexecuted by the ray tracing circuitry. For example, the instructions maybe dispatched and executed on the FUs described above (e.g., vector FU4910, ray tracing FU4912, etc) and/or the graphics cores 3130 or raytracing cores 3150.

When execution is complete for a ray tracing instruction, the resultsare stored at 5006 (e.g., stored back to the memory 3198) and at 5007the primary graphics thread is notified. At 5008, the ray tracingresults are processed within the context of the primary thread (e.g.,read from memory and integrated into graphics rendering results).

In embodiments, the term “engine” or “module” or “logic” may refer to,be part of, or include an application specific integrated circuit(ASIC), an electronic circuit, a processor (shared, dedicated, orgroup), and/or memory (shared, dedicated, or group) that execute one ormore software or firmware programs, a combinational logic circuit,and/or other suitable components that provide the describedfunctionality. In embodiments, an engine, module, or logic may beimplemented in firmware, hardware, software, or any combination offirmware, hardware, and software.

Apparatus and Method for Asynchronous Ray Tracing

Embodiments of the invention include a combination of fixed functionacceleration circuitry and general purpose processing circuitry toperform ray tracing. For example, certain operations related to raytraversal of a bounding volume hierarchy (BVH) and intersection testingmay be performed by the fixed function acceleration circuitry, while aplurality of execution circuits execute various forms of ray tracingshaders (e.g., any hit shaders, intersection shaders, miss shaders,etc). One embodiment includes dual high-bandwidth storage bankscomprising a plurality of entries for storing rays and correspondingdual stacks for storing BVH nodes. In this embodiment, the traversalcircuitry alternates between the dual ray banks and stacks to process aray on each clock cycle. In addition, one embodiment includes priorityselection circuitry/logic which distinguishes between internal nodes,non-internal nodes, and primitives and uses this information tointelligently prioritize processing of the BVH nodes and the primitivesbounded by the BVH nodes.

One particular embodiment reduces the high speed memory required fortraversal using a short stack to store a limited number of BVH nodesduring traversal operations. This embodiment includes stack managementcircuitry/logic to efficiently push and pop entries to and from theshort stack to ensure that the required BVH nodes are available. Inaddition, traversal operations are tracked by performing updates to atracking data structure. When the traversal circuitry/logic is paused,it can consult the tracking data structure to begin traversal operationsat the same location within the BVH where it left off. and the trackingdata maintained in a data structure tracking is performed so that thetraversal circuitry/logic can restart.

FIG. 51 illustrates one embodiment comprising shader execution circuitry4000 for executing shader program code and processing associated raytracing data 4902 (e.g., BVH node data and ray data), ray tracingacceleration circuitry 5110 for performing traversal and intersectionoperations, and a memory 3198 for storing program code and associateddata processed by the RT acceleration circuitry 5110 and shaderexecution circuitry 4000.

In one embodiment, the shader execution circuitry 4000 includes aplurality of cores/execution units 4001 which execute shader programcode to perform various forms of data-parallel operations. For example,in one embodiment, the cores/execution units 4001 can execute a singleinstruction across multiple lanes, where each instance of theinstruction operates on data stored in a different lane. In a SIMTimplementation, for example, each instance of the instruction isassociated with a different thread. During execution, an L1 cache storescertain ray tracing data for efficient access (e.g., recently orfrequently accessed data).

A set of primary rays may be dispatched to the scheduler 4007, whichschedules work to shaders executed by the cores/EUs 4001. The cores/EUs4001 may be ray tracing cores 3150, graphics cores 3130, CPU cores 3199or other types of circuitry capable of executing shader program code.One or more primary ray shaders 5101 process the primary rays and spawnadditional work to be performed by ray tracing acceleration circuitry5110 and/or the cores/EUs 4001 (e.g., to be executed by one or morechild shaders). New work spawned by the primary ray shader 5101 or othershaders executed by the cores/EUs 4001 may be distributed to sortingcircuitry 4008 which sorts the rays into groups or bins as describedherein (e.g., grouping rays with similar characteristics). The scheduler4007 then schedules the new work on the cores/EUs 4001.

Other shaders which may be executed include any hit shaders 4514 andclosest hit shaders 4507 which process hit results as described above(e.g., identifying any hit or the closest hit for a given ray,respectively). A miss shader 4506 processes ray misses (e.g., where aray does not intersect the node/primitive). As mentioned, the variousshaders can be referenced using a shader record which may include one ormore pointers, vendor-specific metadata, and global arguments. In oneembodiment, shader records are identified by shader record identifiers(SRI). In one embodiment, each executing instance of a shader isassociated with a call stack 5203 which stores arguments passed betweena parent shader and child shader. Call stacks 5121 may also storereferences to continuation functions that are executed when a callreturns.

Ray traversal circuitry 5102 traverses each ray through nodes of a BVH,working down the hierarchy of the BVH (e.g., through parent nodes, childnodes, and leaf nodes) to identify nodes/primitives traversed by theray. Ray-BVH intersection circuitry 5103 performs intersection testingof rays, determining hit points on primitives, and generates results inresponse to the hits. The traversal circuitry 5102 and intersectioncircuitry 5103 may retrieve work from the one or more call stacks 5121.Within the ray tracing acceleration circuitry 5110, call stacks 5121 andassociated ray tracing data 4902 may be stored within a local raytracing cache (RTC) 5107 or other local storage device for efficientaccess by the traversal circuitry 5102 and intersection circuitry 5103.One particular embodiment described below includes high-bandwidth raybanks (see, e.g., FIG. 52A).

The ray tracing acceleration circuitry 5110 may be a variant of thevarious traversal/intersection circuits described herein includingray-BVH traversal/intersection circuit 4005, traversal circuit 4502 andintersection circuit 4503, and ray tracing cores 3150. The ray tracingacceleration circuitry 5110 may be used in place of the ray-BVHtraversal/intersection circuit 4005, traversal circuit 4502 andintersection circuit 4503, and ray tracing cores 3150 or any othercircuitry/logic for processing BVH stacks and/or performingtraversal/intersection. Therefore, the disclosure of any features incombination with the ray-BVH traversal/intersection circuit 4005,traversal circuit 4502 and intersection circuit 4503, and ray tracingcores 3150 described herein also discloses a corresponding combinationwith the ray tracing acceleration circuitry 5110, but is not limited tosuch.

Referring to FIG. 52A, one embodiment of the ray traversal circuitry5102 includes first and second ray storage banks, 5201 and 5202,respectively, where each bank comprises a plurality of entries forstoring a corresponding plurality of incoming rays 5206 loaded frommemory. Corresponding first and second stacks, 5203 and 5204,respectively, comprise selected BVH node data 5290-5291 read from memoryand stored locally for processing. As described herein, in oneembodiment, the stacks 5203-5204 are “short” stacks comprising a limitednumber of entries for storing BVH node data (e.g., six entries in oneembodiment). While illustrated separately from the ray banks 5201-5202,the stacks 5203-5204 may also be maintained within the corresponding raybanks 5201-5202. Alternatively, the stacks 5203-5204 may be stored in aseparate local memory or cache.

One embodiment of the traversal processing circuitry 5210 alternatesbetween the two banks 5201-5202 and stacks 5203-5204 when selecting thenext ray and node to process (e.g., in a ping-pong manner). For example,the traversal processing circuitry 5210 may select a new ray/BVH nodefrom an alternate ray bank/stack on each clock cycle, thereby ensuringhighly efficient operation. It should be noted, however, this specificarrangement is not necessary for complying with the underlyingprinciples of the invention.

In one embodiment, a ray allocator 5205 balances the entry of incomingrays 5206 into the first and second memory banks 5201-5202,respectively, based on current relative values of a set of bankallocation counters 5220. In one embodiment, the bank allocationcounters 5220 maintain a count of the number of untraversed rays in eachof the first and second memory banks 5201-5202. For example, a firstbank allocation counter may be incremented when the ray allocator 5205adds a new ray to the first bank 5201 and decremented when a ray isprocessed from the first bank 5201. Similarly, the second bankallocation counter may be incremented when the ray allocator 5205 adds anew ray to the second bank 5201 and decremented when a ray is processedfrom the second bank 5201.

In one embodiment, the ray allocator 5205 allocates the current ray to abank associated with the smaller counter value. If the two counters areequal, the ray allocator 5205 may select either bank or may select adifferent bank from the one selected the last time the counters wereequal. In one embodiment, each ray is stored in one entry of one of thebanks 5201-5202 and each bank comprises 32 entries for storing up to 32rays. However, the underlying principles of the invention are notlimited to these details.

FIG. 52B illustrates four processes 5251-5254 executed in one embodimentto manage the ray storage banks 5201-5202 and stacks 5203-5204. In oneembodiment, the four processes 5251-5254 are different implementationsor configurations of a common set of program code (sometimes referred toherein as “TraceRay”). The Initial process 5251 may be executed to readthe ray 5261 and perform a new top-down traversal of a BVH, startingfrom the root node. The Alloc function modifies control bits andlaunches corresponding read requests to the ray tracing stack. Inparticular, to allocate the new entry, Alloc sets the valid (VLD) bitand resets the evict ready (Evict_Rdy) bit. In the bank entry for theray, the data present (DP) bit and the dirty bit are reset. The DP bitin the corresponding stack entry is set. For the corresponding Hitinfo,the DP bit is set and the dirty bit is reset. The DP bit and the shaderrecord identifier (SRI) DP bit associated with the node data are reset.

The instance process 5252 performs traversal within one of the nodes ofthe BVH (other than the root node) and reads the ray and prior committedhit 5262. In one embodiment, when one of the hit shaders identifies ahit between the ray and a primitive, then the commit process 5253 isexecuted to commit results, reading the ray, the potential hit, and thestack 5263. Alternatively, the continue process 5254 is executed tocontinue traversal of the ray, reading the ray, the committed hit, andthe stack 5264.

In various circumstances, the traversal circuitry 5002 must pausetraversal operations and save the current ray and associated BVH nodes,such as when a shader is required to perform a sequence of operations.For example, if a non-opaque object is hit or a procedural texture, thetraversal circuitry 5002 saves the stack 5203-5204 to memory andexecutes the required shader. Once the shader has completed processingthe hit (or other data), the traversal circuitry 5002 restores the stateof the ray banks 5201-5202 and stacks 5203-5204 from memory.

In one embodiment, a traversal/stack tracker 5248 continually monitorstraversal and stack operations and stores restart data in a trackingarray 5249. For example, if the traversal circuitry 5002 has alreadytraversed nodes N, N0, N1, N2, and N00, and generated results, then thetraversal/stack tracker 5248 will update the tracking array to indicatethat traversal of these nodes has completed and/or to indicate the nextnode to be processed from the stack. When the traversal circuitry 5002is restarted, it reads the restart data from the tracking array 5249 sothat it may restart traversal at the correct stage, withoutre-traversing any of the BVH nodes (and wasting cycles). The restartdata stored in the tracking array 5249 is sometimes referred to as the“restart trail” or “RST.”

As indicated in FIG. 52B, the various TraceRay processes 5251-5254manage allocation into and out of the ray storage banks 5201-5202 viaone or more functions. As illustrated for the initial process 5251, anAlloc function sets the valid bit (VLD) in a storage bank entry(indicating that the entry now contains a valid ray) and resets (Rst)the eviction ready flag (indicating that the ray data should not beevicted). The Ray function stores the ray in the selected entry andresets the data present (DP) bit (indicating that ray data is stored inthe entry) and the dirty bit (indicating that the data has not beenmodified). Upon reading the ray from the storage bank, the Stackfunction sets the DP bit and retrieves the relevant BVH node from thestack (e.g., the root node in the case of the initial process 5251 andanother node in the case of the instance process 5252). The HitInfofunction resets the dirty bit and sets the DP bit for the initialfunction 5251 or resets it for all other functions. In one embodiment,Hitinfo produces data reflecting a ray hit. The Node function resets theDP bit and the SRI (shader record identifier) DP which is the DP forShader Record Identifier. One embodiment performs a Kernel Start Pointer(KSP) lookup to ensure that KSP is not equal to zero. If it is, thendifferent handling is implemented for non-opaque Quads.

In one embodiment, once a ray entry has been allocated in one of thestorage banks 5201-5202 a fetch is performed to retrieve the node data(and potentially other data) from the stack associated with the ray. Inone embodiment, a stack is maintained for each ray, comprising theworking set of data for the current node through which the ray istraversed.

When moving to the next level in the BVH (e.g., upon determining thatthe ray intersects a parent node), the child nodes are sorted and pushedon the stack 5203-5204. The child nodes are popped off the stacksequentially and processed individually to identify child nodes whichthe ray traverses (traversal “hits”). In one embodiment, the stack isstored out to memory or a local cache/storage whenever there is ahandoff between the RT acceleration circuitry 5110 and the shaders 4504,4506, 4507, 5101, 5105.

When a leaf node comprising a quad or triangle (or other primitive type)is identified by the traversal circuitry 5102, it passes thisinformation to the intersection circuitry 5103 which performs anintersection test on the quad or triangle, respectively. If theprimitive is not a quad or triangle then, in one implementation, thetraversal circuitry terminates traversal and passes control back to theclosest hit shader 4507 (if a hit is detected) or the miss shader 4506(if no hit is detected). In an implementation in which the intersectioncircuitry 5103 is designed to perform intersections for a variety ofprimitives in addition to quads and triangles (e.g., lines, arcs,circles, etc), then the traversal circuitry 5102 will forward leaf nodesfor these primitives to the intersection circuitry 5103.

In one embodiment, when a hardware or software component generates aread request to memory 3198 or cache, a 16-bit tag is used to provideinformation about the data type and requestor. For example, a two-bitcode may specify whether the request is for a ray, stack data, hit data,node data from the BVH, or any other type of data. When the ray, stack,and Hitinfo has been returned from memory, the ray is traversed throughone or more BVH nodes and intersection testing is performed as describedabove.

One or more stacks 5203-5204 and rays 5206 are loaded from memory atdifferent processing stages. For example, the initial process 5251and/or instance process 5252 may require a new BVH to be loaded fortraversal. In these circumstances, the stack 5203-5204 may beinitialized to the top node (or “root” node) of the BVH. For a raycontinuation 5254 within a BVH, the stack 5203-5204 may be loaded frommemory and expanded. Once the stack 5203-5204 has been prepared, nodedata is fetched from the stack (an operation sometimes referred to belowas Proc_Node_Fetch).

In one embodiment, node data is fetched by launching parallel requestsfor two non-internal (NI) nodes and two internal nodes. FIG. 53illustrates one such embodiment in which NI node priority selectionlogic (PRISEL) 5311 requests dual NI nodes: a first NI node 5301 fromBank 0 and a second NI node 5302 from Bank 1. Concurrently, InternalNode PRISEL logic 5312 requests dual internal nodes: a first node 5303from Bank 0 and a second node 5304 from Bank 1.

In one embodiment, NI node priority selection logic (PRISEL) 5311prioritizes one of the first NI node 5301 and second NI node 5302,storing the prioritized result in the ray tracing cache (RTC).Similarly, Internal Node PRISEL logic 5312 requests dual internal nodes,and selects a prioritized result from a first internal node 5303 and asecond internal node 5304.

Each instance of the priority selection logic 5311-5312 prioritizes oneof the non-internal BVH nodes 5301-5302 and one of the internal BVHnodes 5303-5304 from a different bank if possible. In one embodiment,only one request is selected from each bank (e.g., one of requests 5302and 5304 and one of requests 5301 and 5303). The launch of theserequests may also reset the stack data present (DP) bit, as indicated,so that this entry is not retrieved in response to a node fetchoperation. In one embodiment, for the instance fetch operation, theray's data present (DP) bit is reset when the instance request is sent,and finally set when the ray is transformed after the node fetch.

In one embodiment, node_info is written at the launch of reads and theaddress/tag is calculated as follows for the reads requests:

-   -   i.        rtt_rtc_rd_addr[47:6]=rt_ray.rt_ray_ctrl.root_node_ptr[47:6]+curr_stack.child_offset;        (Note: The Child offset on the node is always with respect to        Current BVH Root Node)    -   ii. rtt_rtc_rd_tag[6:0]={RTT_INST, rtt_alloc_entry[5:0]};    -   iii. node.node_info=curr_stack.node_info.

In one embodiment, the node data returned will set the DP bit for thenode and the stack.

The following cases can be distinguished based on the read tag:

-   -   A. Internal Node: This will write to the node    -   B. Instance: This will update the rt_ray.rt_ray_ctrl for next        level BVH (1) and write the Node Structure.        -   i. root_node_ptr=node_return.StartNodePtr        -   ii.            hitgrp_srbase_ptr=rt_ray_ctrl.hitgrp_srbase_ptr+rt_ray_ctrl.srstride*node_return.instancecontributiontohitgrpindex        -   iii.            hitgrp_sr_stride=rt_ray_ctrl.srstride*rt_ray_ctrl.shade_indx_mult        -   iv.            inst_leaf_ptr=rt_ray.rt_ray_ctrl.root_node_ptr+stack.current_node.child_offset→Just            Logical view, grab and store the node fetch address during            Instance Node fetch request itself        -   v. {miss_sr_ptr, shader_indx_mult,            mask}={rt_ray[0].rt_ray_ctrl.miss_sr_ptr,            rt_ray[0].rt_ray_ctrl. shader_indx_mult,            rt_ray[0].rt_ray_ctrl.mask} □Preserve BVH[0]        -   vi.            flag[0]=rt_ray[0].rt_ray_ctrl.flag[0]|(˜rt_ray[0].rt_ray_ctrl.flag[1]&            Node_Return.flag[2]); →Either Preserve Opaque via Ray or Via            Instance Flag (only if Ray Flag is not Force Non-Opaque)        -   vii.            flag[1]=(rt_ray[0].rt_ray_ctrl.flag[1])|(˜rt_ray[0].rt_ray_ctrl.flag[0]&            Node_Return.flag[3]); →Either Preserve Non Opaque via Ray or            Via Instance Flag (only if Ray Flag is not Force Opaque)        -   viii. flag[3:2]=rt_ray[0].rt_ray_ctrl.flag[3:2]; →(Accept            FIRST HIT and end Search or Skip Closest Hit Shader)            Preserve BVH[0]        -   ix. flag[5:4]=Node_Return.flag[0]?2′d0:            rt_ray[0].rt_ray_ctrl.flag[5:4]; →Triangle Culling is            disabled VIA Instance        -   x. flag[8:6]=rt_ray[0].rt_ray_ctrl.flag[8:6]; →(Disable            intersection shader, Cull Opaque or Cull Non-Opaque)            Preserve BVH[0]        -   xi. node.node_ctrl=Not Needed for instance        -   xii. node.node_data={′0, node_rtn.obj2world_p,            world2obj_vzyx};    -   C. Quad: This will update the node as follows        -   i. node.node_ctrl={node_rtn.leafDesc.last,            node_rtn.leafDesc.PrimIndex1Delta[15:0],            node_rtn.leafDesc.PrimIndex0[31:0], node_rtn.shader_indx};        -   ii. node.node_data={′0, Quad_mode, J[2:0], V[3:0]};            →Quad_mode=node_rtn.leafDesc.PrimIndex1Delta[15:0]!=′0;

Based on the ray flag, instance flag, and the geometry flag, theopaque/non-opaque handling table shown in FIG. 55A indicates theresulting flag to be used when the node data is fetched (opaque ornon-opaque). As indicated in the table, ray flags always takeprecedence. Additionally, some of the states are mutually exclusive. Inone embodiment, these are handled in hardware with the priority ofexclusive bits. In one implementation, if cull_opaque and force_opaqueare both set, the associated geometry will automatically be culled.

-   -   opaque=rt_ray.rt_ray_ctrl.flag[0]|quad.flag[0]; (Note the Ray        Stored per BVH Level is already accounting for the instance        Flags)    -   nopaque=rt_ray.rt_ray_ctrl.flag[1]|˜ quad.flag[0];

FIG. 55B is a table showing ray flag handling and exceptions inaccordance with one embodiment. Here the decision to cull is based on acombination of the ray flag, instance flag, and geometry flag.

-   -   cull_opaque=rt_ray.rt_ray_ctrl.flag[6]&        (rt_ray.rt_ray_ctrl.flag[0]|quad.flag[0]);    -   cull_nopaque=rt_ray.rt_ray_ctrl.flag[7]&        (rt_ray.rt_ray_ctrl.flag[1]|˜quad.flag[0]);    -   cull=cull_opaque|cull_nopaque;

A mask-based cull may be implemented as follows in one embodiment:

-   -   mask_kill=˜|(rtc_rtt_rd_rtn.mask & rtc_rtt_rd_rtn.data.mask);

FIG. 55C is a table showing final culling in accordance with oneembodiment. The Ray Flag being (cull_opaque and force_opaque) or(cull_non_opaque and force_non_opaque) are mutually exclusive. However,in this equation the Ray Flag is also accounting for the instance flagwhich can set the opaque/non-opaque. Only Geometry can be culled whereasboth instance and geometry can be masked.

As illustrated in FIG. 56 , in one embodiment, based on the evaluationof the cull and mask_kill settings described above, early out isdetermined at 5601 or 5602 and the result either sent to node storage at5603 and/or the stack at 5604.

Once the node data is ready, box/intersection tests may be performed.This is accomplished in one embodiment by a process referred to hereinas Ray_Test_Proc which has two underlying concurrent processes running,one to fill the quad/instance (QI) and another to perform thebox/intersection testing. In one implementation illustrated in FIG. 57 ,Ray_Test_Proc launches two parallel instances of priority selectionlogic (PRISEL) 5701-5702: a quad/instance PRISEL 5701 for requesting andselecting between a quad/instance 5711 from Bank 0 and a secondquad/instance 5712 from Bank 1, and an internal node PRISEL 5702 forrequesting and selecting between an internal node from Bank 0 5713 andan internal node from Bank 1 5714.

In one embodiment, the quad/instance priority selection logic 5701prioritizes one of the first QI node 5711 and second QI node 5712,storing the prioritized result in the ray tracing queue (RTQ) forfurther processing (e.g., intersection testing). Similarly, internalnode PRISEL logic 5702 prioritizes one of the internal BVH nodes5713-5714 on which a ray tracing traversal (RTT) box test is performed.In one embodiment, only one request is selected from each bank (e.g.,one of requests 5711 and 5712 and one of requests 5713 and 5714). Thelaunch of these requests may also reset the stack data present (DP) bit,as indicated, so that this entry is not retrieved in response to a nodefetch operation. In one embodiment, for the instance fetch operation,the ray's data present (DP) bit is reset when the instance request issent, and finally set when the ray is transformed after the node fetch.

As part of this process, for every quad test dispatch where the nodetype is non-opaque, the shader record identifier null lookup isdispatched as a bindless thread dispatch (BTD) based on the followingshader record identifier lookup address:

-   -   sri_null_lookup_ptr[47:3]=2*(Ray.hitGroupSRBasePtr+Node.leafDesc.ShaderIndex*ray.SRStride)+1;    -   sri_null_lookup_tag[7:0]={1′d0, RTT_INST, rtt_alloc_entry[5:0]};

In one embodiment, a quad/instance (QI) decouple FIFO is included toresolve temporal stack FIFO full conditions and to implement synchronousupdates to the hitinfo/ray with a push into the stack FIFO (see, e.g.,stack FIFO 6001 in FIG. 60 ). This is done so that the ray/hitinfo has aguaranteed data present (DP) bit set in subsequent processes. Note thatray/hitinfo may be assigned a fixed high priority when colliding withmemory writes.

The return from RTQ can result in an Instance (e.g., an instancetransformation) or a Quad (i.e., traversal/intersection test results) ontwo separate interfaces. Below are the two return FIFOs used forprocessing results in one embodiment:

-   -   a. Instance Return FIFO: Update        rt_ray.rt_ray_data=rtq_rt_ray_data; ray_dirty[Entry]=1;    -   b. Quad Return FIFO:        -   i. If the Quad is non-opaque and (T_(far)            <P_(rev_)T_(far))+Check SRI_NULL_DP to pop (read from) the            quad/instance (QI) decoupled FIFO. Note that in one            embodiment the Hitinfo write from the ray tracing queue            (RTQ) FIFO has higher priority over MemHitInfo.            -   1. If (KSP_NULL=1)→Treat the non-opaque quad as if it                were opaque and update T_(far).            -   2. If (KSP_NULL!=1)→            -   Write the potential HitInfo to memory with the valid bit                set to 1.            -   Read T, U, V, Leaf Type, PrimLeafIndex, and Front Face                from the RTQ.            -   Read PrimIndexDelta, PrimleafPtr from NodeData. Update                instanceLeafPtr from Ray Data.            -   hitGroupRecPtr as computed above        -   ii. If the quad is non-opaque and (T_(far)            <P_(rev_)T_(far))→            -   Update the Committed HitInfo with Valid=1.            -   Read T,U,V, Leaf Type, PrimLeafIndex, Front Face from                the RTQ.            -   Read PrimIndexDelta, PrimleafPtr from NodeData.            -   Update instanceLeafPtr from rt_ray.rt_ray_ctrl            -   hitGroupRecPtr as computed for above

In one embodiment, the return from the ray tracing traversal (RTT) boxintersection test may push into the stack 0/1 (5203/5204) FIFO 6001 forfurther processing.

FIGS. 58 and FIGS. 59A-B illustrate an example of BVH-ray processingusing a “short” stack (e.g., such as stacks 5203 or 5204, which includea limited number of local stack entries). A short stack is used toconserve high speed storage in combination with intelligent nodemanagement techniques to provide a highly efficient sequence oftraversal operations. In the illustrated example, the short stack 5203includes entries for six BVH nodes. However, the underlying principlesof the invention may be implemented using short stacks of various sizes.

Operations 5949-5972 push and pop stack entries during BVH traversal. Inone embodiment, the operations 5949-5972 are performed on the stack 5203by stack processing circuitry 5120 (see FIG. 51 ). A specific traversalsequence is shown starting with the root BVH node N 5900 at BVH level 0.

At 5949 the stack 5203 is initialized with node N, which is then poppedfrom the stack and processed, resulting in hits H0-H2 comprising childnodes N0-N2 5901-5903 at Level 1 of the BVH (i.e., “hits” meaning thatray traverses the three child nodes N0-N2 5901-5903). The three childnode hits 5901-5902 are sorted based on hit distance and pushed on thestack 5203 (operation 5950) in the sorted order. Thus, in thisembodiment, whenever a new set of child nodes are evaluated, they aresorted based on hit distance and written into the stack 5203 in thesorted order (i.e., with the closer child nodes at the top of thestack).

The first child node NO 5901 (i.e., the closest child node) is poppedfrom the stack 5203 and processed, resulting in three more child nodehits N00-N02 5911-5913 at Level 2 of the BVH (the “level” is sometimesreferred to as the “depth” of the BVH nodes), which are sorted andpushed to the stack 5203 (operation 5951).

Child node N00 5911 is popped from the stack and processed, resulting ina single hit comprising a single child node N000 5920 at Level 3 of theBVH (operation 5952). This node is popped and processed, resulting insix hits N0000-N0005 5931-5936 at level 4, which are sorted and pushedto the stack 5203 (operation 5953). To make room within the short stack5203, nodes N1, N2, N02, N01 are removed as indicated (i.e., to limitthe short stack to six entries). The first sorted node N0000 5931 ispopped and processed, generating three hits N00000-N00002 5931-5933 atLevel 5 of the BVH (operation 5954). Note N0005 is removed to make roomon the short stack 5203 for the new nodes.

In one embodiment, each time a node is removed from the short stack5203, it is saved back to memory. It will then be re-loaded to the shortstack 5203 at a later time (e.g., when it is time to process the node inaccordance with the traversal operation).

Processing continues on FIG. 59A where nodes N00001 and N00002 arepopped and processed (operations 5955-5956) at Level 5 of the BVH. NodesN0001, N0002, N0003, and N0004 at Level 4 are then popped and processed(operations 5957-5960), resulting in an empty short stack 5203.

Thus, a pop operation results in retrieval of the root BVH node, Node Nin accordance with the restart trail (RST) (operation 5961). The threechild hits N0, N1, N2, from Level 1 are again sorted and pushed to theshort stack (operation 5962). Node NO is then popped and processed,followed by Nodes N00, N000, and N0005 (operations 5963-5965). Node N01is popped and processed (operation 5966), followed by Node N02, Node N2,and Node N1 (operations 5967-5970), again resulting in an empty shortstack. Consequently, the next Level 2 node, N11 is popped from the shortstack and processed, completing the traversal (i.e., because Node N11did not result in a hit).

As mentioned, one embodiment of a traversal tracker 5248 updates thetracking array 5249 which identifies the child node/subtree in eachlevel of the BVH hierarchy which is currently being traversed. In oneimplementation, the length of the tracking array 5249 is equal to thedepth of the BVH (6 in the illustrated example) and each entry in thetracking array 5249 includes an index value identifying the childsubtree currently being traversed. In one specific implementation, foran N-wide BVH (i.e., where each internal node references N child nodes)each entry in the tracking array 5249 includes a log 2(N) bit value toidentify the child nodes/subtrees. In one embodiment, childnodes/subtrees assigned an index smaller than the current child indexhave been fully traversed and will therefore will not be revisited inthe event of a restart. In one embodiment, when last intersected childis being traversed, the child index is set to the maximum value toindicate that there are no more entries on the stack.

The short traversal stack 5203 may store the top few entries of thestack in a circular array. In one implementation, each stack entry inthe short traversal stack 5203 includes an offset to a node,miscellaneous information such as the node type (internal, primitive,instance etc.) as well as one bit that indicates if this child is thelast (farthest) intersected child node in a parent node. However, thesespecific details are not required for complying with the underlyingprinciples of the invention.

FIG. 60 illustrates one embodiment of the stack processingcircuitry/logic 5120 for performing stack management and traversaloperations as described above. A stack FIFO 6001 is loaded with anychild BVH nodes 6000 which require processing. For example, when a boxtest or quad test is completed by the traversal processing circuitry5210, the results are pushed into the stack FIFO 6001 and used to updatethe stack 5203. This may include, for example, updates to the hit infosuch as the set of child nodes 6000 associated with a particular hit.

Stack processing circuitry/logic 6003 reads entries from the stack 5203with data required for processing each entry including an indication asto whether the BVH node is an internal node or a leaf node andassociated index data. If the node is a leaf node/quad, then the datamay include quad descriptors and indices as well as shader index data.The stack processing circuitry/logic 6003 then performs the stackprocessing operations described herein such as identifying new nodesassociated with a hit and sorting the nodes based on hit distance.Although illustrated as a separate entity, the stack processingcircuitry/logic 6003 may be integrated within the traversal circuitry5102.

As indicated, the stack processing circuitry/logic 6003 generates stackupdates 6011 as it completes processing each BVH node from the stack5203. For example, after reading an entry from the stack 5203, it mayupdate the various control bits such as the data present (DP) bit andvalid (VLD) bit. FIG. 60 illustrates the evict ready and data presentbits 6010 being set. A corresponding stack update 6011 may also be sentto the stack 5203 (e.g., allowing old entries to be removed to make roomfor new child nodes).

Stack updates may be controlled via arbitration circuitry 6012 whichselects between updating the stack 5203 with the current processingupdates 6011, filling the stack 5203 from memory with one or more newBVH child nodes (Mem Fill), and performing an initial allocation to thestack from memory (e.g., starting with the root node and one or morechild nodes).

In one embodiment, when a quad/instance/internal node is processed onthe stack, one or more of the following operations may be performed:

-   -   i. Eviction of the stack entry due to multiple conditions such        as moving down the instance for a new BVH, processing a hit        procedural, an any hit shader, etc.    -   ii. Deallocate the Ray entry if the stack is evicted due to a        hit procedural and/or any hit shader.    -   iii. Deallocate the cache entry if that stack is evicted due to        hit procedural and/or any hit shader.    -   iv. Update the ray control (BVH only) if the ray needs to be        passed down via the instance leaf to the new BVH.

FIGS. 61A-B illustrate tables for configuring read/write ports andsetting control bits for all ray tracing traversal structures. Inparticular, example sub-structures, vertical structures, and read/writeactions are shown for rays 6101, hits 6102, and stacks 6103. Note,however, that the underlying principles of the invention are not limitedto these specific data structures/operations.

Apparatus and Method for High Quality Ray-Traced Level of DetailTransitions

On graphics processing architectures, the “level-of-detail” (LOD) canrefer to the selection of mesh resolutions based on variables such asdistance from the camera. LOD techniques are used to reduce memoryconsumption and improve graphics processing functions such as geometricaliasing in games. For example, the details of a high resolution meshmay not be required when the mesh is far away from the currentperspective of the user.

In rasterization-based implementations, smooth transitions between LODsare enabled using “stochastic LOD” techniques such as described in Lloydet al, Implementing Stochastic Levels of Detail with Microsoft DirectXRaytracing (Jun. 15, 2020). Without these stochastic techniques, thetransition between LODs can result in distracting artifacts whereobjects suddenly change in appearance when a new LOD is selected. Usingstochastic LODs, a cross-dissolve between LOD levels is performedthrough a random assignment of pixels to one of the LODs involved in thetransition (e.g., either the higher resolution or lower resolution LOD).

The above solution uses a binary mask and a binary comparison value toachieve eight transitional steps for stochastic LOD transitions whenfading from a first LOD (“LOD0”) to a second LOD (“LOD1”). In thisimplementation, an 8-bit ray mask and an 8-bit instance mask arelogically ANDed to determine if an instance needs to be traversed. These8-bit masks and the associated bit-wise logic operations result inlimited LOD transition capabilities. For example, when transitioningbetween LOD0 and LOD1 of an object, where LOD0 has a fractional value of0.25 and LOD1 has a fractional value of 0.75 (based on camera distance),the mask for the instance would be set to LOD0 to enable only 2 randombits (0.25 of 8 bits). The instance mask for LOD1 would be set to thebinary complement of the mask of LOD0, with 6 bits enabled. For anygiven ray, one random bit is selected in the ray-mask to achieve arandom selection of either LOD0 (with a probability of 0.25) and LOD1(with a probability of 0.75). However, because only one of eight bits isselected, there are only 8 intermediate steps for transitioning betweenLOD0 and LOD1.

As shown in FIG. 62 , in one embodiment of the invention, an LODselector 6205 is provided with an N-bit comparison operation mask 6220which is treated as a binary value to determine a comparison operationto be performed. The selected comparison operation is used to compareagainst the reference to allow for more transitional LOD steps. In oneembodiment, the comparison operation is selected fromless-than-or-equal-to (less_equal) and greater-than (greater), althoughthe underlying principles of the invention are not limited to thesespecific comparison operations. In one implementation, 8-bits are used(N=8) where 7 of the bits define an unsigned integer value in the rangeof [0 . . . 127], enabling 128 transitional steps for LOD cross-fadingand 1 bit indicates the comparison operation (e.g., if set to 0, then aless_equal operation is performed and if set to 1, the greater operationis performed). In one embodiment, a ray comparison mask 6221 may also beprovided to the LOD selector 6205 in the range [0 . . . 127] as anadditional ray parameter.

The following code sequence highlights how ray traversal reacts to thisnew comparison mask, in one embodiment:

  if( ray.InstanceMask & instance.InstanceMask ) {  if(  (instance.ComparisonMode == less_equal &&  instance.ComparisonMask < =ray.ComparisonMask ) | |  instance.ComparisonMode == greater &&instance.ComparisonMask  > ray.ComparisonMask )  )  { traverseInstance(Instance);  } }

In the above code sequence, the first IF statement tests whether thebinary masks allow traversal into the current instance. If so, thesecond IF statement then tests the comparison mode setting in view ofthe values for the instance comparison mask (e.g., comparison operationmask 6220) and ray comparison mask 6221.

Returning to the above LOD transition example, for the instance of LOD0with a fractional value of 0.25, the first 7 bits are set to a value of31 (=int(0.25*127)), and the last bit is set to 0 (indicating theless_equal operation). For the instance of LOD1 with a fractional valueof 0.75, the first 7 bits are set to value of 31 (=int((1.0-0.75)*127)),and the last bit is set to 1 (indicating the greater operation). Thus,for this implementation, if a uniformly distributed random number isgenerated in the range [0 . . . 127] as a ray comparison mask, there areup to 127 transitional steps which may be selected by LOD selector 6205for transitioning between LOD0 and LOD1.

While the specific details set forth above are used for the purpose ofexplanation, the underlying principles of the invention may beimplemented with other details. For example, other comparison operatorsmay be used in place of, or in addition to less_equal and greater. Forexample, comparison operators such as not_equal, equal, less andgreater_equal (greater than or equal to) may also be used. Oneimplementation includes a ray flag and an instance flag that disablesANDed ray masks and enables the use of these bits as comparison masks.

Embodiments of the invention include a combination of fixed functionacceleration circuitry and general purpose processing circuitry toperform ray tracing. For example, certain operations related to raytraversal of a bounding volume hierarchy (BVH) and intersection testingmay be performed by the fixed function acceleration circuitry, while aplurality of execution circuits execute various forms of ray tracingshaders (e.g., any hit shaders, intersection shaders, miss shaders,etc). One embodiment includes dual high-bandwidth storage bankscomprising a plurality of entries for storing rays and correspondingdual stacks for storing BVH nodes. In this embodiment, the traversalcircuitry alternates between the dual ray banks and stacks to process aray on each clock cycle. In addition, one embodiment includes priorityselection circuitry/logic which distinguishes between internal nodes,non-internal nodes, and primitives and uses this information tointelligently prioritize processing of the BVH nodes and the primitivesbounded by the BVH nodes.

Acceleration Data Structure Compression

The construction of acceleration data structures is one of the mostimportant steps in efficient ray-traced rendering. In recent times, thebounding volume hierarchy (BVH) acceleration structure, describedextensively herein, has become the most widely used structure for thispurpose. The BVH is a hierarchical tree structure which serves tospatially index and organize geometry such that ray/primitiveintersection queries can be resolved very efficiently. The ability toresolve these queries is one of the most critical operations forray-traced rendering. While the embodiments of the invention describedbelow operate on a BVH structure, the underlying principles of theinvention are not limited to a BVH. These embodiments may be applied toany other acceleration data structure with similar relevant features.

Producing a BVH is typically referred to as “constructing” or “building”the BVH. Although a number of BVH construction algorithms have beenproposed, top-down BVH builders are predominantly used for achievinghigh rendering efficiency for both real-time and offline renderingapplications. Top-down BVH build algorithms typically maintain one ormore temporary arrays during construction. These arrays hold datanecessary to sort/organize geometry to produce the BVH structure. Thesearrays are read and/or written multiple times during the build(typically 1-2 times per level of the BVH hierarchy). As these arraysare often of considerable size, this process is bandwidth-intensive.Thus, improvements in BVH build compute performance, such as could beexpected from a hardware BVH builder, are likely to have only a limitedimpact if this bandwidth issue is not addressed.

One embodiment of the invention includes a compression scheme for thetemporary data maintained by many top-down BVH builders. The purpose ofthis compression scheme is to reduce the bandwidth required for BVHconstruction, thereby enabling faster and more efficient BVHconstruction. Note, however, that the embodiments of the invention maybe used for other kinds of BVH builders and with other types ofacceleration data structures, such as kd-trees.

Many top-down BVH builders maintain two primary types of data during theBVH build: (1) an axis aligned bounding box (AABB) for each primitiveinvolved in the BVH build; and (2) an unsigned integer index associatedwith each primitive, which points to one of these AABBs, and/or to theoriginal primitive from which the AABB was produced.

One embodiment of the invention utilizes a Structure of Arrays (SOA)layout for combining each AABB with a single integer index. The AABBsare maintained in one array, and the integer indices in a second array.Only the index array must be reordered to achieve BVH construction.Storing the build data in this fashion leads to a number of advantages.In this layout scheme, the AABB data is largely read-only, and AABBwrite bandwidth is not incurred for most of the build process.

By using an SOA structure, only the AABBs need to be infrequentlycompressed during the build. In fact, the AABB data may only need to becompressed once before build as a pre-process, depending on theimplementation. Since the build is performed by partitioning the indexarrays, one embodiment of the invention re-compresses these at everylevel of the build.

By operating on compressed versions of these arrays instead of theirconventional, uncompressed counterparts, the bandwidth required for BVHconstruction is reduced. The compressed versions of the arrays arestored temporarily, and used only for the purpose of the build. They arediscarded once build is complete, leaving a BVH which references theoriginal input list of primitives.

An important characteristic of the compression techniques describedherein is that they are cache line-aware. Both of the compressed arraysare stored as an array of Compression Blocks of fixed size, where thesize is a whole number of cache lines. This number is greater than orequal to one. The Compression Blocks of each of the two types of arraydo not need to be the same size. These two types of blocks are referredto herein as AABB Compression Blocks and Index Compression Blocks.

Note that the underlying principles of the invention do not require thatthe size of the blocks is a whole number of cachelines. Rather, this isone of several optional features described herein. In one embodimentdescribed below, this functionality is control by the variablesAABBCompressionBlockSizeBytes and IndexCompressionBlockSizeBytes inTables B and D, respectively.

Because the spatial extent of, and number of primitives referenced by,each node will generally decrease as the top-down build proceeds fromthe root to the leaves of the tree structure, different representationsof the AABBs may be appropriate at different stages of construction. Forexample, the accuracy of the compressed AABBs may be less critical atthe upper levels of the tree, whereas more precise representations maybe needed at the lower levels to maintain reasonable tree quality. Itmay therefore be adequate to use lossy compression near the root of thetree to maximize bandwidth savings, and switch to an uncompressed,lossless representation of the primitives for the lower levels. Thisdivides BVH construction into at least two phases illustrated in FIG. 63: a top phase 6301 for nodes at or above a specified level of thehierarchy (Nodes 0, 1, 8) and a bottom phase 6302 for nodes below thespecified level (Nodes 2-7, 9-14). A multi-level build can proceed insuch a fashion that the entirety of an upper level hierarchy (e.g. the‘Top’ portion in FIG. 63 ) is built before any node in the lower levelsare built, or the building of the levels can be interleaved. If an upperlevel is built entirely before any lower levels, nodes which must besplit at a lower level of the build can be stored on a structure such asa queue to be partitioned at a later stage.

As an alternative to using a full-precision copy of the AABBs for thelower levels 6302, another variation of the scheme is to “re-compress”the AABBs during build for use in building the lower levels. By doingso, geometry can be compressed relative to the extent of individualsubtrees. Since individual subtrees generally represent a smallerspatial extent compared to the root node, this can benefit the accuracyof the compressed representation, or the efficiency of compression. Asimilar pattern for a multi-level compressed build is observed incurrent research. The divide 6300 between different phases ofconstruction can be defined according to a variety of nodecharacteristics. One embodiment uses a fixed number of primitives to actas a threshold value.

A variation used in some embodiments of the invention instead opt toemploy a single-level build only. For example, a single, compressedrepresentation of the build data could be used to build the entire tree.

I. AABB Compression

In one embodiment of the invention, the input to the AABB compressionlogic (which may be implemented in hardware and/or software) is an arrayof uncompressed primitives and the output is an array of AABBcompression blocks, which are of a fixed size, and aligned to somenumber of cache lines. Since the effective AABB compression ratio at anyparticular region of the mesh is highly data-dependent, one embodimentpacks a variable number of AABBs per AABB compression block.

As shown in FIG. 64 , one embodiment of the compression block 6400 isorganized in two main parts: MetaData 6401 and Vector Residuals 6402.The MetaData 6401 provides per-block information and constants requiredto decode the Vector Residuals 6402 into a list of AABBs. The VectorResiduals 6402 store the bulk of the compressed information used torepresent the AABBs. Each of these elements are described in more detailbelow.

Briefly, in one embodiment, delta compression is used. A seedVectorcomprises a baseline set of AABB values and the vector residuals 6402provide offsets to these baseline values to reconstruct each AABB. ThenumResiduals value specifies the number of vector residuals 6402 and theresidualSizeVector specifies the size of the residuals 6402.

AABB Global Compression Constants

In addition to the per-block constants that are stored in eachcompression block 6400, a set of AABB Global Compression Constants maystore information relating to all of the blocks in the entirecompression process. These are summarized in Table B for one particularimplementation.

TABLE B Constant Description NQ { X, Y, Z} Three values which denote thenumber of bits used for quantization of vertex components in each of thethree spatial dimensions. AABBCompressionBlockSize- Size in Bytes of anAABB Compression Bytes Block. This value will typically be aligned to acertain number of cache lines. maxAABBsPerBlock The maximum number ofAABBs allowed in an AABB Compression Block. This constant is used alongwith the numResidualVectorsPerPrimitive Global Compression Constant todetermine the number of bits needed for the numResiduals value shown inFIG. 64. numResidualVectorsPerPrimitive This value keeps track of thenumber of residual vectors being used to represent an AABB in thecompressed blocks. A regular AABB normally consists of two 3D vectors,min and max. However, it is possible that the representation of the AABBcan be transformed to a structure with a different number of vectors. Anexample of this is discussed in the later section on Error! Referencesource not found., where a pair of 3D vectors are transformed to asingle 6D vector. It is necessary for the compression algorithm to keeptrack of this value to perform a number of core operations correctly.residualNumDimensions This constant is used to keep track of how manydimensions the residual vectors will have at the point they are added tothe AABB Compression Blocks. This value is needed as it is possible forthe 3D AABB data to be transformed to a different number of dimensionsduring compression.

AABB Compression Flow

One embodiment of the AABB compression process involves iteratingthrough the input array of primitives in turn, and outputting an arrayof AABB Compression Blocks 6400. The output array contains a minimalnumber of AABB Compression Blocks 6400 needed to represent the AABBs ofthe primitives in compressed form.

FIG. 65 illustrates a process in accordance with one particularembodiment. As mentioned, the compression process is not limited to anyparticular architecture and may be implemented in hardware, software, orany combination thereof.

At 6501 an array of primitives for a BVH build is provided. At 6502, thenext primitive in the array (e.g., the first primitive at the start ofthe process) is selected and its AABB is evaluated for compression. Ifthe AABB fits within the current compression block, determined at 6503(e.g., based on its mix/max data), then the AABB is added to the currentcompression block at 6504. As mentioned, this can include determiningresidual values for the AABB by calculating the distances to an existingbase vector within the compression block (e.g., the seedVector).

In one embodiment, if the AABB of the primitive does not fit within thecompression block, then the current compression block is finalized at6510 and stored in memory within the output array. At 6511, a newcompression block is initialized using the AABB of the primitive. In oneembodiment, the primitive AABB is used as the seed vector for the newcompression block. Residuals may then be generated for subsequent AABBsof primitive based on distances to the new seed vector. In oneimplementation, the first residual, generated for the second AABB, isdetermined based on distance values to the seed vector values. Thesecond residual, for the third AABB, is then determined based ondistances to the first residual. Thus, a running difference is stored,as described in greater detail below. Once the current primitive iscompressed, the process returns to 6502 where the next primitive in thearray is selected for compression.

Thus, visiting each primitive in turn, its AABB is determined (e.g., asa float value). A series of operations are then performed to the AABB toachieve compression and the compressed result is added to the currentAABB Compression Block in the output array. If the compressed AABB fits,it is added to the current block, and the process moves to the nextAABB. If the AABB does not fit, the current AABB Compression Block isfinalized, and a new AABB Compression block is initialized in the outputarray. In this way, the number of compressed blocks needed to store theAABBs is minimized.

The pseudocode below in TABLE C shows the flow of AABB compressionaccording to one particular embodiment of the invention. Note, however,that the underlying principles of the invention are not necessarilylimited to these details.

As shown in the pseudocode sequence, for each AABB Compression Block, aninteger is written in a separate array (blockOffsets) which records theposition in the original primitive array at which each AABB CompressionBlock starts (i.e., the first primitive AABB it contains). TheblockOffsets array is used during the build for resolving the originalprimitive IDs that the compressed block represents.

AABB Residual Computation

In one embodiment, each input AABB goes through a set of stages tocompress it before adding it to a compressed block, resulting in theVector Residuals shown in FIG. 64 . The process is captured as the codeon line 26 of Table C, where the CompressionCore is used to convert theAABB to a list of compressed vectors.

TABLE C 1: uint numBoxesEncoded = 0; 2 : uint blockStartIndex = 0; 3:uint currentBlock = 0; 4 CompressedAABBBlock compressedBlocks = [ ] 5:uint blockOffsets = [ ] 6: uint totalNumBoxes =geometry.getNumPrimitives ( ) ; 7: uint maxBitsPerBlock =AABBCompressionBlockSizeBytes * 8; 8: uint numBitsRequiredCurrentBlock =0; 9: 10: while (numBoxes Encoded < totalNumBoxes) 11: { 12: CompressionCore cCore; 13:  InitBlock (compressedBlocks, currentBlock);14:  blockOffsets.append (numBoxesEncoded); 15:  blockStart Index =numBoxesEncoded; 16:  numBitsRequiredCurrentBlock = 0; 17: 18:  while(numBitsRequiredCurrentBlock < maxBitsPerBlock && 19:    numBoxesEncoded< totalNumBoxes && 20:    (numBoxesEncoded − blockStart Index) <   maxAABBsPerBlock) 21:  { 22 :   Primitive p = geometry.getPrimitive(numEncoded) ; 23:   AABB box = p.getBoundingBox ( ); 24: 25:   VectorcompressedVectors = [ ]; 26:   compressedVectors = cCore.compress (box);27:   numBitsRequiredCurrent Block = 28:    TestAddToBlock(compressedBlocks [currentBlock] ,    compressedVectors); 29: 30:   if(numBitsRequiredCurrentBlock <= maxBitsPerBlock) 31:   { 32:   CommitToBlock (compressedBlocks [currentBlock],   compressedVectors); 33:    numBoxesEncoded++; 34:   } 35:   else 36:   break; 37:  } 38: 39:  FinalizeBlock (compressedBlocks[currentBlock++]); 40: } 41 : 42: if (numBoxesEncoded − blockStartIndex > 0) 43:  FinalizeBlock (compressedBlocks [currentBlock++]);

In one embodiment, compression of an AABBs occurs in the followingstages: (1) quantization, (2) transform, and (3) prediction/deltacoding.

1. Quantization

In one embodiment, the floating-point AABB values are first quantized toan unsigned integer representation using a fixed number of bits peraxis. This quantization step may be performed in a variety of ways. Forexample, in one implementation, the following values for each axis i aredetermined:

L _(i) =S _(max,i) −S _(min,i)

N _(B,i)=2^(NQ) ^(i)

VU _(min,i)=(VF _(min,i) −S _(min,i))/L _(i) ×N _(B,i)

VU _(max,i)=(VF _(max,i) −S _(min,i))/L _(i) ×N _(B,i)

where S_(min) and S_(max) are the minimum and maximum coordinates of theentire set of geometry for which a BVH is to be built, N_(B,i) is thenumber of cells in the quantized grid in the i-th axis, NQ_(i)corresponds to the value in Table B, VU_(min) and VU_(max) are theminimum and maximum coordinates of the quantized AABB, VF_(min) andVF_(max) are the minimum and maximum coordinates of the originalfloating-point AABB, and the subscript i denotes a given axis(i∈{x,y,z}). As any floating-point computation can introduce error, theintermediate values should be rounded up or down to minimize the valuesof VU_(min) and maximize the values of VU_(max). The values may also beconverted to integer and clamped to the valid range, to ensure awatertight AABB residing inside the AABB of the entire set of geometry.

S_(min) and S_(max) could also represent the extent of a subset of thegeometry (e.g. a subtree within a larger BVH). This could occur, forexample, in a multi-level compressed build as per FIG. 63 .

2. Transform

In one embodiment, a transform stage is implemented in which data istransformed into a form that is more amenable to compression. Although avariety of transforms may be used, one embodiment of the inventionemploys a novel transform referred to herein as Position-ExtentTransform, which combines VU_(min) and VU_(max) into a single 6dimensional (6D) vector per primitive, VT, as shown below:

E _(x) =VU _(max,x) −VU _(min,x) E _(y) =VU _(max,y) −VU _(min,y) E _(z)=VU _(max,z) −VU _(min,z)

V _(T)=(VU _(min,x) ,VU _(min,y) ,VU _(min,z) ,E _(x) ,E _(y) ,E _(z))

where VU_(min){x,y,z} and VU_(max){x,y,z} are the components of VU_(min)and VU_(max) respectively. Essentially, this transform allows theposition and extent/size characteristics of the AABB to be treatedseparately in the remaining compression stages. As mentioned, othertransforms may also be used.

3. Prediction/Delta Coding

In one implementation, a conventional delta coding technique is used toachieve good compression performance. In one embodiment, the firstvector in each compression block is designated as a “seed” vector andstored verbatim in the AABB compression block 6400, as shown in FIG. 64. For subsequent vectors, a running difference of the values is stored(i.e., residuals 6402). This corresponds to a prediction scheme wherethe prediction for the next input vector in the sequence is always theprevious input vector, and the residual value is the difference betweenthe current and previous input vectors. Residual values 6402 in thisembodiment are thus signed values, which requires an additional signbit. Various other prediction/delta coding may be used while stillcomplying with the underlying principles of the invention.

One embodiment stores the residual values 6402 with the minimum numberof required bits, in order to maximize compression. Based on the size ofthe residual values at the end of the residual coding steps, a certainnumber of bits will be required for each of the vector dimensions toaccommodate the range of values encountered in that dimension.

The number of bits required are stored in a Residual Size Vector (RSV),as illustrated in the metadata 6401 in FIG. 64 . The RSV is fixed for agiven compression block 6400, and so all values in a given dimension ofa particular block use the same number of bits for their residuals 6402.

The value stored in each element of the RSV is simply the minimum numberof bits needed to store the entire range of residual values in thedimension as a signed number. While compressing a given AABB CompressionBlock (i.e. lines 18-37 of Table C), a running maximum of the number ofbits needed to accommodate all the vectors seen so far is maintained.The RSV is determined for each newly-added AABB (i.e. CommitToBlock,line 32 of Table C) and stored in the compression blocks' metadata.

To test whether a new AABB will fit into the current block (i.e.TestAddToBlock, line 28 of Table C and operation 6503 in FIG. 65 ), wecompute the expected new RSV that would occur from adding the new AABB,sum the expected RSV vector, and then multiply this value by the totalnumber of residuals that would exist in the block if the new AABB wasadded. If this value is within the budget available for storingresiduals (i.e. less than or equal to the total block size minus themeta data 6401 size), it can be added to the current block. If not, thena new compression block is initialized.

Entropy Coding

One embodiment of the invention includes an additional step to the AABBresidual computation which includes an entropy coding of the residualsafter prediction/delta coding. The underlying principles of theinvention are not limited to this particular implementation.

Pre-Sorting/Re-Ordering Capability

As an optional pre-process, the input geometry can be sorted/re-orderedto improve spatial coherence, which may improve compression performance.Sorting can be performed in a variety of ways. One way to achieve thisis to use a Morton Code sort. Such a sort is already used as major stepin other BVH builders to promote spatial coherence in the geometrybefore extracting a hierarchy.

The compressed AABBs can be written in any desired order, but if theAABBs are reordered/sorted, then it is necessary to store an additionalarray of integers which records the sorted ordering. The array consistsof a single integer index per primitive. The build can proceed with theprimary index used to reference the re-ordered list of primitives. Whenthe original primitive ID is needed (such as when the contents of a leafnode are being written), we must use the primary index to look up theoriginal primitive ID in the additional array to ensure that the treereferences the original input geometry list correctly.

II. AABB Decompression

In one embodiment, decompression of the AABBs is performed for an entireAABB Compression Block 6400 at a time. The residual data is firstreconstructed by inspecting the metadata 6401 of the compression block6400 and interpreting the stored residuals based on this information(e.g., adding the distance values to the seed vector and prior residualvalues in the sequence). The inverse of each of the AABB CompressionStages is then performed to decompress the single-precision floatingpoint AABBs represented by the compression block.

One embodiment implements a variation of the decompression step in thecase of BVH builders which employ reduced-precision constructiontechniques which are aligned to a compressed hierarchy output. Suchreduced-precision builders are described in the co-pending applicationentitled “An Architecture for Reduced Precision Bounding VolumeHierarchy Construction”, Ser. No. 16/746,636, Filed Jan. 17, 2020, whichis assigned to the assignee of the present application. Areduced-precision builder performs much of its computation in areduced-precision, integer space. Consequently, one embodiment of theinvention aligns the quantization step of the AABB Residual Computationdescribed herein with the quantization employed in the reduced-precisionbuilder. The AABBs may then be decompressed to integer only, alignedwith the coordinate space of whatever node is currently being processedby the reduced-precision builder. A similar variation may be implementedwith a builder which does not output a compressed hierarchy, butperforms quantization of vertices.

III. Index Compression

In one embodiment of the invention, the index array is compressed intoan array of Index Compression Blocks. FIG. 66 illustrates one embodimentof an index compression block 6610 comprising metadata 6603 and indexresiduals 6602. The index array differs from the AABB array as it mustbe re-compressed as the indices are partitioned/reordered during thebuild process.

In many conventional BVH builders, indices are represented as unsignedintegers, generally with one index per primitive. The purpose of theindex array is to point to primitive AABBs. Each AABB/primitive may beallocated a fixed size in memory. It is therefore possible to randomlyaccess any particular primitive p or AABB a in the arrays. However, whenAABB compression leads to a variable number of AABBs per cache line, theAABB compression block storing a given primitive is not easilydetermined after compression. Storing conventional indices is thereforenot compatible with the AABB Compression Blocks described herein.

In one embodiment of the invention, the indexing techniques used toidentify the location of primitive AABBs also allow for compression ofthe indices themselves. Two novel techniques are referred to below asBlock Offset Indexing (BOI) and Hierarchical Bit-Vector Indexing (HBI).These indexing implementations may be used alone or in combination inthe various embodiments of the invention. In addition, both indexingtechniques can be used as part of a multi-level build, as per FIG. 63 ,and both types of indices may also be used as part of the same BVHbuild. These indexing techniques allow the BVH build to proceed in asimilar manner to a conventional BVH builder, but with compressedrepresentations of both the AABB and the corresponding index arrays.

Global Index Compression Constants

Index compression employs a set of Global Index Compression Constants,which apply to all Index Compression Blocks. Both of the indexcompression schemes described below share the same global constants,which are summarized in Table D below.

TABLE D Constant Description IndexCompressionBlockSizeBytes Size inBytes of an Index Compression Block. This value will typically bealigned to a certain number of cache lines. maxIndicesPerBlock Themaximum number of indices allowed in an Index Compression Block. Thisvalue determines the number of bits needed to store the number ofindices represented by a given block.

Block Offset Indexing

In Block Offset Indexing (BOI), the regular single-integer index ischanged to a structure containing two integers, one of which identifiesthe compression block 6400 and one of which comprises an offset toidentify the primitive AABB data within the compression block 6400. Oneembodiment of the new data structure is generated in accordance with thefollowing code sequence:

  struct blockOffsetIndex   {  uint blockIdx;  uint blockOffset; }Here, blockIdx stores an index to an AABB Compression Block, andblockOffset references a specific primitive AABB inside the block (i.e.,blockIdx in combination with blockOffset provides the address of theprimitive AABB). This information is sufficient to fully reference aparticular AABB within its compression block during a build.

In one embodiment, one of these structures is generated for eachprimitive in the BVH build, so the size of the list is predictable.However, given a variable number of AABBs per AABB Compression Block,there will be a variable number of these index structures for each ofthese compression blocks (e.g., not all possible values of blockOffsetwill exist for each AABB Compression Block). Therefore, to correctlyinitialize the array of Block Offset Indices, it is necessary to referto the blockOffsets array (see, e.g., the code sequence in Table C),from which the number of primitives in each AABB Compression Block canbe determined, either concurrently with, or as a post-process to, theAABB compression. Once initialized, the Block Offset Indices can betreated in essentially the same manner as conventional indices found inconventional BVH builders.

Single-integer indices used in conventional BVH builders are typically 4bytes in size. In one embodiment, 26 bits are used for blockIdx and 6bits are used for blockOffset. In an alternate embodiment, smallernumbers of bits are used for each variable to reduce the overall memoryfootprint. In one embodiment, since a fixed size for the blockOffsetmust be chosen, this places limits on the maximum number of primitivesper AABB Compression Block. In the case of 6 bits, a maximum of 64primitives can be represented per AABB Compression Block.

The remaining item to address for Block Offset Indexing is howcompression can be achieved. Block Offset Indices are delta coded andpacked in order into Index Compression Blocks. Each block is packed withas many indices as possible, and a new Index Compression Block isstarted each time the previous one reaches capacity. This is performedin a very similar manner to the AABB Compression Blocks (as shown inTable C), leading to a variable number of indices per Index CompressionBlock.

FIG. 66 illustrates one example of a block offset index compressionblock 6610 comprising metadata 6603 identifying the number of indices inaddition to a residual size vector and seed vector. In one embodiment, atwo-channel encoding is used for the index residuals 6602, where theblockIdx and blockOffset values are separately delta-compressed. Similarto AABB Compression Blocks, the index compression block 6610 stores anindication of the number of indices in the block, the number of bits forthe residuals (as the residual size vector), and a seed vectorcomprising a first seed vector for blockIdx and a second seed vector forblockOffset. The index residual values 6602 comprise a pair ofdifference values resulting from compression. For example, an indexresidual value may comprise a first difference value representing adifference between the current input blockIdx value and a prior inputblockIdx value and a second difference value representing a differencebetween the current input blockOffset value and a prior inputblockOffset value. The first blockIdx and blockOffset values in thesequence are stored verbatim in the seedVector field, which representsthe vector from which the first residual value is computed.

Hierarchical Bit-Vector Indexing

One embodiment of the invention uses another primitive index compressiontechnique referred to as Hierarchical Bit-Vector Indexing (HBI), whichmay be used alone or in combination with Block Offset Indexing (BOI).HBI is unlike both conventional integer indices and BOI in that a singleHBI Index can reference multiple primitives at once. In fact, an HBIIndex can reference up to an entire AABB Compression Block.

An expanded structure of this type of index is shown in FIGS. 67A-B.Each HBI index 6700 consists of two elements. The blockIdx 6708 pointsto a given AABB Compression Block, serving the same purpose as thecorresponding element in Block Offset Indices. The second component is abit vector 6701 which has a number of bits equal to the maximum numberof AABBs allowed in an AABB Compression Block (i.e., maxAABBsPerBlock).Each bit in the bit vector 6701 signifies if the corresponding elementin the AABB Compression Block is referenced by this index. For example,if the third bit in the bit-vector is a ‘1’, this signifies that thethird AABB/primitive of the AABB Compression Block is referenced by theHBI index. If the bit is ‘0’, then that AABB/primitive is notreferenced.

In contrast to BOI indices, a single HBI index 6700 per AABB CompressionBlock is created when the array is initialized. The blockIdx values 6708are set to ascending values starting from 0, and the initial bit vectors6701 are set to all 1's. As partitioning occurs in the top down builder,if all of the primitives referenced by a given HBI index 6700 all lie onthe same side of the splitting plane, the index can simply bepartitioned as-is into one side of the list, similar to a conventionalinteger index. However, if the HBI index 6700 references primitives onboth sides of a splitting plane, then the index must be split into twonew HBI indices, with one HBI index being placed in each of the two newindex sub-lists corresponding to the left and right partitions. To splitan HBI index, the index is duplicated and the bit-vectors 6701 areupdated in each copy of the index to reflect the primitives referencedby the two new indices. This means that the number of HBI indices in thearray can grow, and the duplication of indices is somewhat similar tohow spatial splits are handled in some conventional BVH builders. Asimple way to handle the potentially growing list is simply to allocatea “worst-case” amount of memory.

HBI indices 6700 can be packed into Index Compression Blocks using deltacompression on the blockIdx components 6708. In addition, HBI indicesalso offer a hierarchical compression opportunity from which they derivetheir name. Any HBI index which does not straddle a splitting plane willhave all elements of its bit-vector equal to ‘1’. When packing HBIindices into Index Compression Blocks, a single-bit flag may be used(sometimes referred to herein as a bit-vector occupancy flag) toindicate that the entire bit-vector is “all 1s”. A value of ‘0’indicates that the bit-vector is stored verbatim in the block, and avalue of ‘1’ indicates that the vector is “all 1s” and thus is notstored at all (except for the flag). Thus, HBI indices derivecompression from two techniques: delta coding and hierarchicalbit-vectors. Like BOI indices, HBI indices are also packed intocompression blocks in a very similar manner to AABB Compression Blocks.To perform this correctly, the compression operation must also monitorthe index bit-vectors to decide if any bit-vectors must be storedverbatim, and factor this into the required size for the block.

FIG. 67B shows how a sequence of HBI indices can be coded into an HBIcompression block 6710 including residual data 6704 and metadata 6701.In this embodiment, the residual data includes blockIdx residuals 6702and hierarchical membership bit-vectors 6703. HBI indexing is intendedto operate near the top of the hierarchy, or near the tops of subtreesfor which the AABB Compression Blocks have recently been recompressed,as per a multi-level build situation of FIG. 63 . This is because HBIindices are impacted more directly by changing spatial coherence in theAABB Compression Blocks compared to other indexing methods. In fact,although HBI indices provide compression, the worst-case situation canactually result in an expansion of the index data (up to an upperbound). Transitioning to Block Offset Indexing (BOI) or conventionalinteger indices mid-build can avoid this situation, and may be moreeffective if re-compression has not been recently performed.

Index Transitions Between Build Levels

If either BOI or HBI indices are used in a BVH build, and the buildtransitions to another stage (as per a multi-level build situation ofFIG. 63 ), then it will be necessary to decode the indices to a formthat is appropriate for the next build stage. For example, in the simplecase of using Block Offset Indexing for the upper levels of the tree,and transitioning from a compressed AABB representation to aconventional AABB representation, then it will be necessary to decodethe Block Offset Indices into conventional integer indices. The BlockOffset Indices can be discarded after the transition.

A similar transition will need to occur for HBI indexing, and fortransitioning between two compressed build levels employing differentAABB compression configurations. The transition process is relativelysimple, as both Block Offset Indices and Hierarchical Bit-Vector indicesrepresent alternative encodings of the same underlying information, andcan also always be decoded to conventional integer indices thatreference the original set of primitives.

Partitioning Compressed Index Arrays

In top-down BVH builds, it is necessary to partition/sort the list ofinteger indices in order to recurse during the build and for the indexordering to reflect the tree structure. In conventional BVH builders,this step is straightforward, as the indices are a regular, uncompresseddata structure. However, the embodiments of the invention describedherein result in a new challenge in that a list of Index CompressionBlocks must be partitioned rather than a list of indices. Moreover, itis not possible to predict the number of blocks until after all of theindices are compressed. As the indices are re-compressed after eachpartitioning step, this challenge is present throughout the build.

Although it is not possible to predict the size of the compressed indexarray in advance, we can place an upper bound on the maximum size of thearray, if we know the number of indices to be compressed. In a top-downBVH builder, the number of indices in each index sub-array resultingfrom a node partition is typically known before the partitioning occurs,and so an upper bound can be derived for both sub-arrays at eachpartitioning step.

In the case of BOI, the maximum size of the array occurs when nocompression of the indices is achieved by delta compression. Byfactoring in the size of the metadata for a block, it is possible topredict the maximum number of blocks, and thus the maximum size inbytes.

In the case of HBI indexing, the maximum size occurs when no deltacompression of the blockIdx is achieved, and the HBI indices are splitto such an extent that each HBI index represents only a single primitive(only one bit is set in each index bit-vector). By factoring in all ofthe metadata, include the additional bit used for the first level of thehierarchical bit-vector (the bit-vector occupancy flag), we can computethe maximum number of blocks, and thus the maximum size in bytes for agiven number of primitives.

Given that an upper bound can be placed on the size of the array, asimple technique is used to partition the Index Compression Block arrayusing a pair of arrays. Both arrays are sized to the maximum possiblesize based on the index type, as discussed previously in this section.At the beginning of the build, a set of initial indices is written toone of the arrays in the pair. For each level, blocks from one array areread, interpreted, and newly compressed blocks written out to the secondarray which reflect the partitioned indices. On recursion, the roles ofeach of the arrays can be switched, always reading from the array thathas just been written. Since the ordering of the indices is changing toreflect the partitioning, the index arrays are continually recompressed.

Since the maximum number of blocks in a partition can be predicted, eachsub-array resulting from a partition can be written in a position of theother array such that the maximum size can always be accommodated. Thiscan effectively lead to “gaps” in the arrays, but still achievesbandwidth compression. If partitioning indices in this way, the BVHbuilder may keep track of the start/end of the current build task interms of the Index Compression Blocks referencing its primitives, aswell as the number of primitives in the build task.

Spatial Splits

A widely used technique to improve BVH traversal efficiency in somecases is the use of spatial splits. As the AABBs are not recompressed ateach level of the build, it is difficult to incorporate spatial splitswhich occur during the build itself (as is seen in some related works)into the compression scheme. However, the compression scheme should becompatible with a pre-splitting approach, as per other previous designs.Such schemes deliver a set of AABBs to the BVH build, and generallyrequire little or no modification to the build itself.

One way to combine these pre-splitting schemes with the embodiments ofthe invention is to prepare the array of float AABBs in advance,including all split primitives (rather than computing them as per line23 of Table C), and to keep an array of IDs linking them back to theoriginal primitives. We could then use the BOI or HBI indices, orconventional indices, to reference these AABBs during the build, andlink them back to the original primitives when required (such as whenwriting leaf nodes).

FIG. 68 illustrates one embodiment of a ray tracing engine 8000 of a GPU2505 with compression hardware logic 6810 and decompression hardwarelogic 6808 for performing the compression and decompression techniquesdescribed herein. Note, however, that FIG. 68 includes many specificdetails which are not required for complying with the underlyingprinciples of the invention.

A BVH builder 6807 is shown which constructs a BVH based on a currentset of primitives 6806 (e.g., associated with a current graphics image).In one embodiment, BVH compression logic 6810 operates in concert withthe BVH builder 6807 to concurrently compress the underlying data usedby the BVH builder 6807 to generate a compressed version of the data6812. In particular, the compression logic 6810 includes a bounding boxcompressor 6825 to generate AABB compression blocks 6400 and indexcompressor 6826 to generate index compression blocks 6610 as describedherein. While illustrated as a separate unit in FIG. 68 , thecompression logic 6810 may be integrated within the BVH builder 6807.Conversely, a BVH builder is not required for complying with theunderlying principles of the invention.

When a system component requires uncompressed data 6814 (e.g., such asthe BVH builder 6807), decompression logic 6808 implements thetechniques described herein to decompress the compressed data 6812. Inparticular, an index decompressor 6836 decompresses the indexcompression blocks 6610 and bounding box decompressor 6835 decompressesthe AABB compression blocks 6400 to generate uncompressed AABBs of theuncompressed data 6814. The uncompressed data 6814 may then be accessedby other system components.

The various components illustrated in FIG. 68 may be implemented inhardware, software, or any combination thereof. For example, certaincomponents may be executed on one or more of the execution units 4001while other components such as the traversal/intersection unit 6803 maybe implemented in hardware.

Moreover, the primitives 6806, compressed data 6812, and uncompresseddata 6814 may be stored in a local memory/cache 6898 and/or a systemmemory (not shown). For example, in a system that supports sharedvirtual memory (SVM), the virtual memory space may be mapped across oneor more local memories and the physical system memory. As mentionedabove, the BVH compression blocks may be generated based on the size ofcache lines in the cache hierarchy (e.g., to fit one or more compressionblocks per cache line).

Apparatus and Method for Displaced Mesh Compression

One embodiment of the invention performs path tracing to renderphotorealistic images, using ray tracing for visibility queries. In thisimplementation, rays are cast from a virtual camera and traced through asimulated scene. Random sampling is then performed to incrementallycompute a final image. The random sampling in path tracing causes noiseto appear in the rendered image which may be removed by allowing moresamples to be generated. The samples in this implementation may be colorvalues resulting from a single ray.

In one embodiment, the ray tracing operations used for visibilityqueries rely on bounding volume hierarchies (BVHs) (or other 3Dhierarchical arrangement) generated over the scene primitives (e.g.,triangles, quads, etc) in a preprocessing phase. Using a BVH, therenderer can quickly determine the closest intersection point between aray and a primitive.

When accelerating these ray queries in hardware (e.g., such as with thetraversal/intersection circuitry described herein) memory bandwidthproblems may arise due to the amount of fetched triangle data.Fortunately, much of the complexity in modeled scenes is produced bydisplacement mapping, in which a smooth base surface representation,such as a subdivision surface, is finely tessellated using subdivisionrules to generate a tessellated mesh 6991 as shown in FIG. 69A. Adisplacement function 6992 is applied to each vertex of the finelytessellated mesh which typically either displaces just along thegeometric normal of the base surface or into an arbitrary direction togenerate a displacement mesh 6993. The amount of displacement that isadded to the surface is limited in range; thus very large displacementsfrom the base surface are infrequent.

One embodiment of the invention effectively compressesdisplacement-mapped meshes using a lossy watertight compression. Inparticular, this implementation quantizes the displacement relative to acoarse base mesh, which may match the base subdivision mesh. In oneembodiment, the original quads of the base subdivision mesh may besubdivided using bilinear interpolation into a grid of the same accuracyas the displacement mapping.

FIG. 69B illustrates compression circuitry/logic 6900 that compresses adisplacement mapped mesh 6902 in accordance with the embodimentsdescribed herein to generate a compressed displaced mesh 6910. In theillustrated embodiment, displacement mapping circuitry/logic 6911generates the displacement-mapped mesh 6902 from a base subdivisionsurface. FIG. 70A illustrates an example in which a primitive surface7000 is finely tessellated to generate the base subdivision surface7001. A displacement function is applied to the vertices of the basesubdivision surface 7001 to create a displacement mapping 7002.

Returning to FIG. 69B, in one embodiment, a quantizer 6912 quantizes thedisplacement-mapped mesh 6902 relative to a coarse base mesh 6903 togenerate a compressed displaced mesh 6910 comprising a 3D displacementarray 6904 and base coordinates 6905 associated with the coarse basemesh 6903. By way of example, and not limitation, FIG. 70B illustrates aset of difference vectors d1-d4 7022, each associated with a differentdisplaced vertex v1-v4.

In one embodiment, the coarse base mesh 7003 is the base subdivisionmesh 6301. Alternatively, an interpolator 6921 subdivides the originalquads of the base subdivision mesh using bilinear interpolation into agrid of the same accuracy as the displacement mapping.

The quantizer 6912 determines the difference vectors d1-d4 7022 fromeach coarse base vertex to a corresponding displaced vertex v1-v4 andcombines the difference vectors 7022 in the 3D displacement array 6904.In this manner, the displaced grid is defined using just the coordinatesof the quad (base coordinates 6905), and the array of 3D displacementvectors 6904. Note that these 3D displacement vectors 6904 do notnecessarily match to the displacement vectors used to calculate theoriginal displacement 7002, as a modelling tool would normally notsubdivide the quad using bilinear interpolation and apply more complexsubdivision rules to create smooth surfaces to displace.

As illustrated in FIG. 70C, grids of two neighboring quads 7090-7091will seamlessly stitch together, as along the border 7092, both quads7090-7091 will evaluate to the exact same vertex locations v5-v8. As thedisplacements stored along the edge 7092 for neighboring quads 7090-7091are also identical, the displaced surface will not have any cracks. Thisproperty is significant, as this, in particular means that the accuracyof the stored displacements can be reduced arbitrarily for an entiremesh, resulting in a connected displaced mesh of lower quality.

In one embodiment, half-precision floating point numbers are used toencode the displacements (e.g., 16-bit floating point values).Alternatively, or in addition, a shared exponent representation is usedthat stores just one exponent for all three vertex components and threemantissas. Further, as the extent of the displacement is normally quitewell bounded, the displacements of one mesh can be encoded using fixedpoint coordinates scaled by some constant to obtain sufficient range toencode all displacements. While one embodiment of the invention usesbilinear patches as base primitives, using just flat triangles, anotherembodiment uses triangle pairs to handle each quad.

A method in accordance with one embodiment of the invention isillustrated in FIG. 71 . The method may be implemented on thearchitectures described herein, but is not limited to any particularprocessor or system architecture.

At 7101 a displacement-mapped mesh is generated from a base subdivisionsurface. For example, a primitive surface may be finely tessellated togenerate the base subdivision surface. At 7102, a base mesh is generatedor identified (e.g., such as the base subdivision mesh in oneembodiment).

At 7103, a displacement function is applied to the vertices of the basesubdivision surface to create a 3D displacement array of differencevectors. At 7104, the base coordinates associated with the base mesh aregenerated. As mentioned, the base coordinates may be used in combinationwith the difference vectors to reconstruct the displaced grid. At 7105the compressed displaced mesh is stored including the 3D displacementarray and the base coordinates.

The next time the primitive is read from storage or memory, determinedat 6506, the displaced grid is generated from the compressed displacedmesh at 7103. For example, the 3D displacement array may be applied tothe base coordinates to reconstruct the displaced mesh.

Enhanced Lossy Displaced Mesh Compression and Hardware BVHTraversal/Intersection for Lossy Grid Primitives

Complex dynamic scenes are challenging for real-time ray tracingimplementations. Procedural surfaces, skinning animations, etc., requireupdates of triangulation and accelerating structures in each frame, evenbefore the first ray is launched.

Instead of just using a bilinear patch as base primitive, one embodimentof the invention extends the approach to support bicubic quad ortriangle patches, which need to be evaluated in a watertight manner atthe patch borders. In one implementation, a bitfield is added to thelossy grid primitive indicating whether an implicit triangle is valid ornot. One embodiment also includes a modified hardware block that extendsthe existing tessellator to directly produce lossy displaced meshes(e.g., as described above with respect to FIGS. 69A-71 ), which are thenstored out to memory.

In one implementation, a hardware extension to the BVH traversal unittakes a lossy grid primitive as input and dynamically extracts boundingboxes for subsets of implicitly-referenced triangles/quads. Theextracted bounding boxes are in a format that is compatible with the BVHtraversal unit's ray-box testing circuitry (e.g., the ray/box traversalunit 8930 described below). The result of the ray vs. dynamicallygenerated bounding box intersection tests are passed to theray-quad/triangle intersection unit 8940 which extracts the relevanttriangles contained in the bounding box and intersects those.

One implementation also includes an extension to the lossy gridprimitive using indirectly referenced vertex data (similar to otherembodiments), thereby reducing memory consumption by sharing vertex dataacross neighboring grid primitives. In one embodiment, a modifiedversion of the hardware BVH triangle intersector block is made aware ofthe input being triangles from a lossy displaced mesh, allowing it toreuse edge computation for neighboring triangles. An extension is alsoadded to the lossy displaced mesh compression to handle motion blurredgeometry.

As described above, assuming the input is a grid mesh of arbitrarydimensions, this input grid mesh is first subdivided into smallersubgrids with a fixed resolution, such as 4×4 vertices as illustrated inFIG. 72 .

As shown in FIG. 73 , in one embodiment a lossy 4×4 grid primitivestructure (GridPrim) is now computed based on the 4×4 input vertices.One implementation operates in accordance with the following codesequence:

    struct GridPrim {  PrimLeafDesc leafDesc;  // 4B  uint32_tprimIndex; // 4B  float3  vertex[4];   // 48B  struct {    exp : 7; //shared exponent    disp_x : 5;    disp_y : 5;    disp_z : 5;  } disp_mag[16]; // 44B }; // 64 bytes total

In one implementation, these operations consume 100 bytes: 18 bits fromPrimLeafDesc can be reserved to disable individual triangles, e.g., abit mask of (in top-down, left-right order) 000000000100000000b woulddisable the highlighted triangle 7401 shown in FIG. 74 .

Implicit triangles may be either 3×3 quads (4×4 vertices) or moretriangles. Many of these stitch together forming a mesh. The mask tellsus whether we want to intersect the triangle. If a hole is reached,deactivate the individual triangles per the 4×4 grid. This enablesgreater precision and significantly reduced memory usage: ˜5.5bytes/triangle, which is a very compact representation. In comparison,if a linear array is stored in full precision, each triangle takes 48and 64 bytes.

As illustrated in FIG. 75 , a hardware tesselator 7550 tessellatespatches to triangles in 4×4 units and stores them out to memory so BVHscan be built over them and they can be ray-traced. In this embodiment,the hardware tessellator 7550 is modified to directly support lossydisplaced grid primitives. Instead of generating individual trianglesand passing them to the rasterization unit, the hardware tessellationunit 7550 can directly generate lossy grid primitives and store them outto memory.

An extension to the hardware BVH traversal unit 7550 that takes a lossygrid primitive as input and on the fly extracts bounding boxes forsubsets of implicitly referenced triangles/quads. In the example shownin FIG. 76 , nine bounding boxes 7601A-I, one for each quad, areextracted from the lossy grid and passed as a special nine-wide BVH nodeto the hardware BVH traversal unit 7550 to perform ray-box intersection.

Testing all 18 triangles, one after the other, is very expensive.Referring to FIG. 77 , one embodiment extracts one bounding box 7601A-Ifor each quad (although this is just an example; any number of trianglescould be extracted). When a subset of triangles are read and boundingboxes computed, an N-wide BVH node 7700 is generated—one child node7601A-I for each quad. This structure is then passed to the hardwaretraversal unit 7710 which traverses rays through the newly constructedBVH. Thus, in this embodiment, the grid primitive is used an implicitBVH node from which the bounding boxes can be determined. When abounding box is generated, it is known to contain two triangles. Whenthe hardware traversal unit 7710 determines that a ray traverses one ofthe bounding boxes 7601A-I, the same structure is passed to theray-triangle intersector 7715 to determine which bounding box has beenhit. That is, if the bounding box has been hit, intersection tests areperformed for the triangles contained in the bounding box.

In one embodiment of the invention, these techniques are used as apre-culling step to the ray-triangle traversal 7710 and intersectionunits 7710. The intersection test is significantly cheaper when thetriangles can be inferred using only the BVH node processing unit. Foreach intersected bounding box 7601A-I, the two respective triangles arepassed to ray-tracing triangle/quad intersection unit 7715 to performthe ray-triangle intersection tests.

The grid primitive and implicit BVH node processing techniques describedabove may be integrated within or used as a pre-processing step to anyof the traversal/intersection units described herein (e.g., such asray/box traversal unit 8930 described below).

In one embodiment, extensions of such a 4×4 lossy grid primitive areused to support motion-blur processing with two time steps. One exampleis provided in the following code sequence:

  struct GridPrimMB {  PrimLeafDesc leafDesc;  // 4B  uint32_t primIndex; // 4B  float3 vertex_time0[4];   // 48B  float3vertex_time1[4];   // 48B  // total 32 bytes up to here  struct {   exp: 6; // shared exponent   disp_x : 6;   disp_y : 6;   disp_z : 6;  }disp_mag_time0[16],disp_mag_time1[16]; // 2×48B }; // 8 + 96 + 96 bytestotal

Motion blur operations are analogous to simulating shutter time in acamera. In order to ray-trace this effect, moving from t0 to t1, thereare two representations of a triangle, one for t0 and one for t1. In oneembodiment, an interpolation is performed between them (e.g.,interpolate the primitive representations at each of the two time pointslinearly at 0.5).

The downside of acceleration structures such as bounding volumehierarchies (BVHs) and k-d trees is that they require both time andmemory to be built and stored. One way to reduce this overhead is toemploy some sort of compression and/or quantization of the accelerationdata structure, which works particularly well for BVHs, which naturallylend to conservative, incremental encoding. On the upside, this cansignificantly reduce the size of the acceleration structure oftenhalving the size of BVH nodes. On the downside, compressing the BVHnodes also incurs overhead, which may fall into different categories.First, there is the obvious cost of decompressing each BVH node duringtraversal; second, in particular for hierarchical encoding schemes theneed to track parent information slightly complicates the stackoperations; and third, conservatively quantizing the bounds means thatthe bounding boxes are somewhat less tight than uncompressed ones,triggering a measurable increase in the number of nodes and primitivesthat have to be traversed and intersected, respectively.

Compressing the BVH by local quantization is a known method to reduceits size. An n-wide BVH node contains the axis-aligned bounding boxes(AABBs) of its “n” children in single precision floating point format.Local quantization expresses the “n” children AABBs relative to the AABBof the parent and stores these value in quantized e.g. 8 bit format,thereby reducing the size of BVH node.

Local quantization of the entire BVH introduces multiple overheadfactors as (a) the de-quantized AABBs are coarser than the originalsingle precision floating point AABBs, thereby introducing additionaltraversal and intersection steps for each ray and (b) thede-quantization operation itself is costly which adds and overhead toeach ray traversal step. Because of these disadvantages, compressed BVHsare only used in specific application scenarios and not widely adopted.

One embodiment of the invention employs techniques to compress leafnodes for hair primitives in a bounding-volume hierarchy as described inco-pending application entitled Apparatus and Method for CompressingLeaf Nodes of Bounding Volume Hierarchies, Ser. No. 16/236,185, FiledDec. 28, 2018, which is assigned to the assignee of the presentapplication. In particular, as described in the co-pending application,several groups of oriented primitives are stored together with a parentbounding box, eliminating child pointer storage in the leaf node. Anoriented bounding box is then stored for each primitive using 16-bitcoordinates that are quantized with respect to a corner of the parentbox. Finally, a quantized normal is stored for each primitive group toindicate the orientation. This approach may lead to a significantreduction in the bandwidth and memory footprint for BVH hair primitives.

In some embodiments, BVH nodes are compressed (e.g. for an 8-wide BVH)by storing the parent bounding box and encoding N child bounding boxes(e.g., 8 children) relative to that parent bounding box using lessprecision. A disadvantage of applying this idea to each node of a BVH isthat at every node some decompression overhead is introduced whentraversing rays through this structure, which may reduce performance.

To address this issue, one embodiment of the invention uses thecompressed nodes only at the lowest level of the BVH. This provides anadvantage of the higher BVH levels running at optimal performance (i.e.,they are touched as often as boxes are large, but there are very few ofthem), and compression on the lower/lowest levels is also veryeffective, as most data of the BVH is in the lowest level(s).

In addition, in one embodiment, quantization is also applied for BVHnodes that store oriented bounding boxes. As discussed below, theoperations are somewhat more complicated than for axis-aligned boundingboxes. In one implementation, the use of compressed BVH nodes withoriented bounding boxes is combined with using the compressed nodes onlyat the lowest level (or lower levels) of the BVH.

Thus, one embodiment improves upon fully-compressed BVHs by introducinga single, dedicated layer of compressed leaf nodes, while using regular,uncompressed BVH nodes for interior nodes. One motivation behind thisapproach is that almost all of the savings of compression comes from thelowest levels of a BVH (which in particular for 4-wide and 8-wide BVHsmake up for the vast majority of all nodes), while most of the overheadcomes from interior nodes. Consequently, introducing a single layer ofdedicated “compressed leaf nodes” gives almost the same (and in somecases, even better) compression gains as a fully-compressed BVH, whilemaintaining nearly the same traversal performance as an uncompressedone.

FIG. 80 illustrates an exemplary ray tracing engine 8000 which performsthe leaf node compression and decompression operations described herein.In one embodiment, the ray tracing engine 8000 comprises circuitry ofone or more of the ray tracing cores described above. Alternatively, theray tracing engine 8000 may be implemented on the cores of the CPU or onother types of graphics cores (e.g., Gfx cores, tensor cores, etc).

In one embodiment, a ray generator 8002 generates rays which atraversal/intersection unit 8003 traces through a scene comprising aplurality of input primitives 8006. For example, an app such as avirtual reality game may generate streams of commands from which theinput primitives 8006 are generated. The traversal/intersection unit8003 traverses the rays through a BVH 8005 generated by a BVH builder8007 and identifies hit points where the rays intersect one or more ofthe primitives 8006. Although illustrated as a single unit, thetraversal/intersection unit 8003 may comprise a traversal unit coupledto a distinct intersection unit. These units may be implemented incircuitry, software/commands executed by the GPU or CPU, or anycombination thereof.

In one embodiment, BVH processing circuitry/logic 8004 includes a BVHbuilder 8007 which generates the BVH 8005 as described herein, based onthe spatial relationships between primitives 8006 in the scene. Inaddition, the BVH processing circuitry/logic 8004 includes BVHcompressor 8009 and a BVH decompressor 8009 for compressing anddecompressing the leaf nodes, respectively, as described herein. Thefollowing description will focus on 8-wide BVHs (BVH8) for the purposeof illustration.

As illustrated in FIG. 81 , one embodiment of a single 8-wide BVH node8100A contains 8 bounding boxes 8101-8108 and 8 (64 bit) childpointers/references 8110 pointing to the bounding boxes/leaf data8101-8108. In one embodiment, BVH compressor 8025 performs an encodingin which the 8 child bounding boxes 8101A-8108A are expressed relativeto the parent bounding box 8100A, and quantized to 8-bit uniform values,shown as bounding box leaf data 8101B-8108B. The quantized 8-wide BVH,QBVH8 node 8100B, is encoded by BVH compression 8125 using a start andextent value, stored as two 3-dimensional single precision vectors (2×12bytes). The eight quantized child bounding boxes 8101B-8108B are storedas 2 times 8 bytes for the bounding boxes' lower and upper bounds perdimension (48 bytes total). Note this layout differs from existingimplementations as the extent is stored in full precision, which ingeneral provides tighter bounds but requires more space.

In one embodiment, BVH decompressor 8026 decompresses the QBVH8 node8100B as follows. The decompressed lower bounds in dimension/can becomputed byQBVH8.start_(i)+(byte-to-float)QBVH8.lower_(i)*QBVH8.extend_(i), whichon the CPU 4099 requires five instructions per dimension and box: 2loads (start,extend), byte-to-int load+upconversion, int-to-floatconversion, and one multiply-add. In one embodiment, the decompressionis done for all 8 quantized child bounding boxes 8101B-8108B in parallelusing SIMD instructions, which adds an overhead of around 10instructions to the ray-node intersection test, making it at least morethan twice as expensive than in the standard uncompressed node case. Inone embodiment, these instructions are executed on the cores of the CPU4099. Alternatively, the a comparable set of instructions are executedby the ray tracing cores 4050.

Without pointers, a QBVH8 node requires 72 bytes while an uncompressedBVH8 node requires 192 bytes, which results in reduction factor of2.66×. With 8 (64 bit) pointers the reduction factor reduces to 1.88×,which makes it necessary to address the storage costs for handling leafpointers.

In one embodiment, when compressing only the leaf layer of the BVH8nodes into QBVH8 nodes, all children pointers of the 8 children8101-8108 will only refer to leaf primitive data. In one implementation,this fact is exploited by storing all referenced primitive data directlyafter the QBVH8 node 8100B itself, as illustrated in FIG. 81 . Thisallows for reducing the QBVH8's full 64 bit child pointers 8110 to just8-bit offsets 8122. In one embodiment, if the primitive data is a fixedsized, the offsets 8122 are skipped completely as they can be directlycomputed from the index of the intersected bounding box and the pointerto the QBVH8 node 8100B itself.

When using a top-down BVH8 builder, compressing just the BVH8 leaf-levelrequires only slight modifications to the build process. In oneembodiment these build modifications are implemented in the BVH builder8007. During the recursive build phase the BVH builder 8007 trackswhether the current number of primitives is below a certain threshold.In one implementation N×M is the threshold where N refers to the widthof the BVH, and M is the number of primitives within a BVH leaf. For aBVH8 node and, for example, four triangles per leaf, the threshold is32. Hence for all sub-trees with less than 32 primitives, the BVHprocessing circuitry/logic 8004 will enter a special code path, where itwill continue the surface area heuristic (SAH)-based splitting processbut creates a single QBVH8 node 8100B. When the QBVH8 node 8100B isfinally created, the BVH compressor 8009 then gathers all referencedprimitive data and copies it right behind the QBVH8 node.

The actual BVH8 traversal performed by the ray tracing core 8150 or CPU8199 is only slightly affected by the leaf-level compression.Essentially the leaf-level QBVH8 node 8100B is treated as an extendedleaf type (e.g., it is marked as a leaf). This means the regular BVH8top-down traversal continues until a QBVH node 8100B is reached. At thispoint, a single ray-QBVH node intersection is executed and for all ofits intersected children 8101B-8108B, the respective leaf pointer isreconstructed and regular ray-primitive intersections are executed.Interestingly, ordering of the QBVH's intersected children 8101B-8108Bbased on intersection distance may not provide any measurable benefit asin the majority of cases only a single child is intersected by the rayanyway.

One embodiment of the leaf-level compression scheme allows even forlossless compression of the actual primitive leaf data by extractingcommon features. For example, triangles within a compressed-leaf BVH(CLBVH) node are very likely to share vertices/vertex indices andproperties like the same objectID. By storing these shared propertiesonly once per CLBVH node and using small local byte-sized indices in theprimitives the memory consumption is reduced further.

In one embodiment, the techniques for leveraging commonspatially-coherent geometric features within a BVH leaf are used forother more complex primitive types as well. Primitives such as hairsegments are likely to share a common direction per-BVH leaf. In oneembodiment, the BVH compressor 8009 implements a compression-schemewhich takes this common direction property into account to efficientlycompress oriented bounding boxes (OBBs) which have been shown to be veryuseful for bounding long diagonal primitive types.

The leaf-level compressed BVHs described herein introduce BVH nodequantization only at the lowest BVH level and therefore allow foradditional memory reduction optimizations while preserving the traversalperformance of an uncompressed BVH. As only BVH nodes at the lowestlevel are quantized, all of its children point to leaf data 8101B-8108Bwhich may be stored contiguously in a block of memory or one or morecache line(s) 8098.

The idea can also be applied to hierarchies that use oriented boundingboxes (OBB) which are typically used to speed up rendering of hairprimitives. In order to illustrate one particular embodiment, the memoryreductions in a typical case of a standard 8-wide BVH over triangleswill be evaluated.

The layout of an 8-wide BVH node 8100 is represented in the followingcore sequence:

  struct BVH8Node {  float lowerX[8], upperX[8];  // 8 × lower and upperbounds in the X dimension  float lowerY[8], upperY[8];  // 8 × lower andupper bounds in the Y dimension  float lowerZ[8], upperZ[8];  // 8 ×lower and upper bounds in the Z dimension  void *ptr[8];  // 8 × 64bitpointers to the 8 child nodes or leaf data };and requires 276 bytes of memory. The layout of a standard 8-widequantized Node may be defined as:

struct QBVH8Node {  Vec3f start, scale;  char lowerX[8], upperX[8];  //8 x byte quantized lower/upper bounds in the X dimension  charlowerY[8], upperY[8];  // 8 x byte quantized lower/upper bounds in the Ydimension  char lowerZ[8], upperZ[8];  // 8 x byte quantized lower/upperbounds in the Z dimension  void *ptr[8];  // 8 x 64bit pointers to the 8child nodes or leaf data };and requires 136 bytes.

Because only quantized BVH nodes are used at the leaf level, allchildren pointers will actually point to leaf data 8101A-8108A. In oneembodiment, by storing the quantized node 8100B and all leaf data8101B-8108B its children point to in a single continuous block of memory8098, the 8 child pointers in the quantized BVH node 8100B are removed.Saving the child pointers reduces the quantized node layout to:

struct QBVH8NodeLeaf { Vec3f start, scale;  // start position, extendvector of the parent AABB  char lowerX[8], upperX[8];  // 8 x bytequantized lower and upper bounds in the X dimension  char lowerY[8],upperY[8];  // 8 x byte quantized lower and upper bounds in the Ydimension  char lowerZ[8], upperZ[8];  // 8 x byte quantized lower andupper bounds in the Z dimension };which requires just 72 bytes. Due to the continuous layout in thememory/cache 8098, the child pointer of the i-th child can now be simplycomputed by:childPtr(i)=addr(QBVH8NodeLeaf)+sizeof(QBVH8NodeLeaf)+i*sizeof(LeafDataType).

As the nodes at lowest level of the BVH makes up for more than half ofthe entire size of the BVH, the leaf-level only compression describedherein provide a reduction to 0.5+0.5*72/256=0.64× of the original size.

In addition, the overhead of having coarser bounds and the cost ofdecompressing quantized BVH nodes itself only occurs at the BVH leaflevel (in contrast to all levels when the entire BVH is quantized).Thus, the often quite significant traversal and intersection overheaddue to coarser bounds (introduced by quantization) is largely avoided.

Another benefit of the embodiments of the invention is improved hardwareand software prefetching efficiency. This results from the fact that allleaf data is stored in a relatively small continuous block of memory orcache line(s).

Because the geometry at the BVH leaf level is spatially coherent, it isvery likely that all primitives which are referenced by a QBVH8NodeLeafnode share common properties/features such as objectID, one or morevertices, etc. Consequently, one embodiment of the invention furtherreduces storage by removing primitive data duplication. For example, aprimitive and associated data may be stored only once per QBVH8NodeLeafnode, thereby reducing memory consumption for leaf data further.

The effective bounding of hair primitives is described below as oneexample of significant memory reductions realized by exploiting commongeometry properties at the BVH leaf level. To accurately bound a hairprimitive, which is a long but thin structure oriented in space, awell-known approach is to calculate an oriented bounding box to tightlybound the geometry. First a coordinate space is calculated which isaligned to the hair direction. For example, the z-axis may be determinedto point into the hair direction, while the x and y axes areperpendicular to the z-axis. Using this oriented space a standard AABBcan now be used to tightly bound the hair primitive. Intersecting a raywith such an oriented bound requires first transforming the ray into theoriented space and then performing a standard ray/box intersection test.

A problem with this approach is its memory usage. The transformationinto the oriented space requires 9 floating point values, while storingthe bounding box requires an additional 6 floating point values,yielding 60 bytes in total.

In one embodiment of the invention, the BVH compressor 8025 compressesthis oriented space and bounding box for multiple hair primitives thatare spatially close together. These compressed bounds can then be storedinside the compressed leaf level to tightly bound the hair primitivesstored inside the leaf. The following approach is used in one embodimentto compress the oriented bounds. The oriented space can be expressed bythee normalized vectors v_(x), v_(y), and v_(z) that are orthogonal toeach other. Transforming a point p into that space works by projectingit onto these axes:

p _(x) =dot(v _(x) ,p)

p _(y) =dot(v _(y) ,p)

p _(z) =dot(v _(z) ,p)

As the vectors v_(x), v_(y), and v_(z) are normalized, their componentsare in the range [−1,1]. These vectors are thus quantized using 8-bitsigned fixed point numbers rather than using 8-bit signed integers and aconstant scale. This way quantized v_(x)′, v_(y)′, and v_(z)′ aregenerated. This approach reduces the memory required to encode theoriented space from 36 bytes (9 floating point values) to only 9 bytes(9 fixed point numbers with 1 byte each).

In one embodiment, memory consumption of the oriented space is reducedfurther by taking advantage of the fact that all vectors are orthogonalto each other. Thus one only has to store two vectors (e.g., p_(y)′ andp_(z)′) and can calculate p_(x)′=cross(p_(y)′, p_(z)′), further reducingthe required storage to only six bytes.

What remains is quantizing the AABB inside the quantized oriented space.A problem here is that projecting a point p onto a compressed coordinateaxis of that space (e.g., by calculating dot(v_(x)′, p)) yields valuesof a potentially large range (as values p are typically encoded asfloating point numbers). For that reason one would need to use floatingpoint numbers to encode the bounds, reducing potential savings.

To solve this problem, one embodiment of the invention first transformsthe multiple hair primitive into a space, where its coordinates are inthe range [0, 1/√3]. This may be done by determining the world spaceaxis aligned bounding box b of the multiple hair primitives, and using atransformation T that first translates by b.lower to the left, and thenscales by 1/max(b.size.x, b.size.y.b.size.z) in each coordinate:

${T(p)} = {\frac{1}{\sqrt{3}}\left( {p - {b.{lower}}} \right)/{\max\left( {{b.{size}.x},{b.{size}.y},{b.{size}.z}} \right)}}$

One embodiment ensures that the geometry after this transformation staysin the range [0, 1/√3] as then a projection of a transformed point ontoa quantized vector p_(x)′, p_(y)′, or p_(z)′ stays inside the range[−1,1]. This means the AABB of the curve geometry can be quantized whentransformed using T and then transformed into the quantized orientedspace. In one embodiment, 8-bit signed fixed point arithmetic is used.However, for precision reasons 16-bit signed fixed point numbers may beused (e.g., encoded using 16 bit signed integers and a constant scale).This reduces the memory requirements to encode the axis-aligned boundingbox from 24 bytes (6 floating point values) to only 12 bytes (6 words)plus the offset b.lower (3 floats) and scale (1 float) which are sharedfor multiple hair primitives.

For example, having 8 hair primitives to bound, this embodiment reducesmemory consumption from 8*60 bytes=480 bytes to only 8*(6+12)+3*4+4=160bytes, which is a reduction by 3×. Intersecting a ray with thesequantized oriented bounds works by first transforming the ray using thetransformation T, then projecting the ray using quantized v_(x)′, v_(y)′and v_(z)′. Finally, the ray is intersected with the quantized AABB.

The fat leaves approach described above provides an opportunity for evenmore compression. Assuming there is an implicit single float3 pointer inthe fat BVH leaf, pointing to the shared vertex data of multipleadjacent GridPrims, the vertex in each grid primitive can be indirectlyaddressed by byte-sized indices (“vertex_index_*”), thereby exploitingvertex sharing. In FIG. 78 , vertices 7801-7802 are shared—and stored infull precision. In this embodiment, the shared vertices 7801-7802 areonly stored once and indices are stored which point to an arraycontaining the unique vertices. Thus, instead of 48 bytes only 4 bytesare stored per timestamp. The indices in the following code sequence areused to identify the shared vertices.

struct GridPrimMBIndexed {    PrimLeafDesc leafDesc; // 4B    uint32_tprimIndex; // 4B    uint8_t vertex_index_time0[4]; // 4B    uint8_tvertex_index_time1[4]; // 4B  // total 16 bytes up to here    struct {    exp  : 5; // shared exponent     disp_x : 5;     disp_y : 5;    disp_z : 5;    } disp_mag_time0[16],disp_mag_time1[16]; // 80  bytes }; // 96 bytes total

In one embodiment, shared edges of primitives are only evaluated once toconserve processing resources. In FIG. 79 , for example, it is assumedthat a bounding box consists of the highlighted quads. Rather thanintersecting all triangles individually, one embodiment of the inventionperforms ray-edge computations once for each of the three shared edges.The results of the three ray-edge computations are thus shared acrossthe four triangles (i.e., only one ray-edge computation is performed foreach shared edge). In addition, in one embodiment, the results arestored to on-chip memory (e.g., a scratch memory/cache directlyaccessible to the intersector unit).

Atomics for Graphics and Data Structures

An “atomic” is a set of operations which must be completed as a singleunit. Certain atomics would be beneficial for graphics processingperformance, especially when executing compute shaders. One embodimentof the invention includes a variety of new atomics to improve graphicsprocessing performance, including:

-   -   Atomics that clamp    -   ‘z-tested’ atomic writes    -   ‘z-tested’ atomic accumulation    -   Atomics for ring-buffers

I. Atomics for Clamping

One embodiment of a clamping atomic specifies a destination, type value,and minimum and maximum clamping values. By way of example, a clampingatomic may take the form:

-   -   InterlockedAddClamp(destination, type value, type min, type max)        The above clamping operation atomically adds a value to the        destination and then clamps to the specified minimum and maximum        values (e.g., setting to the maximum for any values above the        maximum and setting to the minimum for any values below min).

Clamping atomic values may be 32 bits, 64 bits, or any other data size.Moreover, clamping atomics may operate on various data types including,but not limited to, uint, float, 2xfp16, float2, and 4xfp16.

II. “Z-Tested” Scattered Writes

Z-tested scattered writes may be used for a variety of applicationsincluding, for example:

-   -   scattered cube map rendering/voxelization (e.g., for environment        probes);    -   scattered imperfect reflective shadow maps (RSMs) (similar to        imperfect shadow maps but for indirect illumination); and    -   dynamic diffuse global illumination style global illumination        through scattered “environment probe” updates.

The following is an example of a compare exchange instruction which maybe executed in one embodiment of the invention:

-   -   InterlockedCmpXChg_type_cmp_op( )        -   type=int, uint, float        -   cmp_op=less, greater, equal, less_equal, greater_equal,            not_equal e.g.: InterlockedDepthCmpXChg_float_less_equal( )

An example 64-bit destination register 8201 is illustrated in FIG. 82Astoring a 32-bit depth value 8202 and a 32-bit payload 8203. Inoperation, the above compare exchange command only exchanges payload anddepth if the new floating point depth value is less than or equal to thestored float value. In one embodiment, the cmpxchg atomics are “remote”atomics, meaning that the actual compare and atomic update is not doneby the EU which issued the instruction, but instead by a logic blockclose to the LLC (or memory controller) storing the data.

Example High Level Shading Language (HLSL) Intrinsics for Read-WriteByte Address Buffers (RWByteAddressBuffers)

In one embodiment, only the HighCompValue is of the type to be comparedwith the high 32 bits in the 64 bit destination. The rest are assumed tobe converted to 32-bit unsigned integer (asuint( )):

-   -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_Less(uint        byteAddress64, uint uHighCompVal, uint2 HighAndLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_LessEqual(uint        byteAddress64, uint uHighCompVal, uint2 HighAndLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_Greater(uint        byteAddress64, uint uHighCompVal, uint2 HighAndLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_GreaterEqual(uint        byteAddress64, uint uHighCompVal, uint2 HighAndLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_Equal(uint        byteAddress64, uint uHighCompVal, uint2 HighAndLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_NotEqual(uint        byteAddress64, uint uHighCompVal, uint2 HighAndLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_Less(uint        byteAddress64, int iHighCompVal, uint2 HighAndLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_LessEqual(uint        byteAddress64, int iHighCompVal, uint2 HighAndLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_Greater(uint        byteAddress64, int iHighCompVal, uint2 HighAndLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_GreaterEqual(uint        byteAddress64, int iHighCompVal, uint2 HighAndLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_Equal(uint        byteAddress64, int iHighCompVal, uint2 HighAndLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_NotEqual(uint        byteAddress64, int iHighCompVal, uint2 HighAndLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_Less(uint        byteAddress64, float fHighCompVal, uint2 HighAndLowVal, out        uint2 HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_LessEqual(uint        byteAddress64, float fHighCompVal, uint2 HighAndLowVal, out        uint2 HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_Greater(uint        byteAddress64, float fHighCompVal, uint2 HighAndLowVal, out        uint2 HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_GreaterEqual(uint        byteAddress64, float fHighCompVal, uint2 HighAndLowVal, out        uint2 HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_Equal(uint        byteAddress64, float fHighCompVal, uint2 HighAndLowVal, out        uint2 HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareHighExchange_NotEqual(uint        byteAddress64, float fHighCompVal, uint2 HighAndLowVal, out        uint2 HighAndLowOrgVal)

Example HLSL Intrinsics for Destination R

HighCompValue is of the type to be compared with the high 32 bits at the64 bit dest. The rest is assumed to be converted using asuint( )

All these intrinsics take a ‘dest’ parameter of type ‘ R’ which caneither be a resource variable or a shared memory variable. A resourcevariable is a scalar reference to a resource including indexing or fieldreferences. A shared memory variable is one defined with the‘groupshared’ keyword. In either case, the type must be uint2 or uint64.When ‘ R’ is a shared memory variable type, the operation is performedon the ‘value’ parameter and the shared memory register referenced by‘dest’. When ‘ R’ is a resource variable type, the operation isperformed on the ‘value’ parameter and the resource location referencedby ‘dest’. The result is stored in the shared memory register orresource location referenced by ‘ dest’:

-   -   void InterlockedCompareHighExchange_Less(R dest, uint        uHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_LessEqual(R dest, uint        uHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_Greater(R dest, uint        uHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_GreaterEqual(R dest, uint        uHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_Equal(R dest, uint        uHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_NotEqual(R dest, uint        uHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_Less(R dest, int        iHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_LessEqual(R dest, int        iHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_Greater(R dest, int        iHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_GreaterEqual(R dest, int        iHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_Equal(R dest, int        iHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_NotEqual(R dest, int        iHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_Less(R dest, float        fHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_LessEqual(R dest, float        fHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_Greater(R dest, float        fHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_GreaterEqual(R dest, float        fHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_Equal(R dest, float        fHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareHighExchange_NotEqual(R dest, float        fHighCompVal, uint2 HighAndLowVal, out uint2 HighAndLowOrgVal)

III. “Z-Tested” Scattered Accumulation

Two embodiments are described below with respect to FIGS. 82B-C. FIG.82B illustrates a 64-bit destination register storing a 32-bit depthvalue and a 32-bit payload value. FIG. 82C illustrates a 64-bitdestination storing a 32-bit depth value and two 16-bit floating pointvalues. The following is an example atomic:

-   -   InterlockedCmpAdd_type1_type2_cmp_op( )        -   type1=int, uint, float        -   type2=int, uint, float, 2xfp16        -   cmp_op=less, greater, equal, less_equal, greater_equal,            not_equal        -   e.g.: InterlockedCmpAccum_float_2xfp16_less( )            -   if the new float depth value is <the stored float depth                value:                -   2. Exchange the stored depth value with the new one                -   3. Dest.Payload.lowfpl6+=InputPayload.lowfp16                -   4. Dest.Payload.highfp16+=InputPayload.highfp16

New HLSL Intrinsics for RWByteAddressBuffers

Only the HighCompValue is of the type to be compared with the high 32bits at the 64 bit destination. The AddLowVal can be of type,float′,,int′, uint′ and, min16float2′:

-   -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_Less(uint        byteAddress64, uint uHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_LessEqual(uint        byteAddress64, uint uHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_Greater(uint        byteAddress64, uint uHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_GreaterEqual(uint        byteAddress64, uint uHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_Equal(uint        byteAddress64, uint uHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_NotEqual(uint        byteAddress64, uint uHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_Less(uint        byteAddress64, int iHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_LessEqual(uint        byteAddress64, int iHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_Greater(uint        byteAddress64, int iHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_GreaterEqual(uint        byteAddress64, int iHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_Equal(uint        byteAddress64, int iHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_NotEqual(uint        byteAddress64, int iHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_Less(uint        byteAddress64, float fHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_LessEqual(uint        byteAddress64, float fHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_Greater(uint        byteAddress64, float fHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_GreaterEqual(uint        byteAddress64, float fHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_Equal(uint        byteAddress64, float fHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)    -   void        RWByteAddressBuffer::InterlockedCompareExchangeHighAddLow_NotEqual(uint        byteAddress64, float fHighCompVal, type AddLowVal, out uint2        HighAndLowOrgVal)

Suggested New HLSL Intrinsics for Destination R

Only the HighCompValue is of the type to be compared with the high 32bits at the 64 bit dest. The AddLowVal can be of type, float′, int′,uint′ and, min16float2′:

-   -   void InterlockedCompareExchangeHighAddLow_LessEqual(R dest, uint        uHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_Greater(R dest, uint        uHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_GreaterEqual(R dest,        uint uHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_Equal(R dest, uint        uHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_NotEqual(R dest, uint        uHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_Less(R dest, int        iHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_LessEqual(R dest, int        iHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_Greater(R dest, int        iHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_GreaterEqual(R dest,        int iHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_Equal(R dest, int        iHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_NotEqual(R dest, int        iHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_Less(R dest, float        fHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_LessEqual(R dest,        float fHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_Greater(R dest, float        fHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_GreaterEqual(R dest,        float fHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_Equal(R dest, float        fHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)    -   void InterlockedCompareExchangeHighAddLow_NotEqual(R dest, float        fHighCompVal, type AddLowVal, out uint2 HighAndLowOrgVal)

IV. Atomics for Ring Buffers

A ring buffer (or circular buffer) is a data structure comprising asingle, fixed-size buffer which operates as if it were connectedend-to-end. Circular buffers are commonly used for buffering datastreams. One embodiment of the invention include atomics for appendingand popping entries to and from ring buffers.

Initially AppendIndex and PopFrontIndex are 0. In order to atomicallyappend or pop, one embodiment uses special 64-bit atomics. With theseatomics, GPU threads can, for example, implement a producer-consumerscheme within the limits of the capacity of the ring buffer. A hardwarewatchdog can wake up kernels that wait on the ring buffer.

The following code sequences illustrate atomic operation for appendingand popping entries from a ring buffer in accordance with one embodimentof the invention:

a. Ring Buffer Append

InterlockedAppend( in dest64, in RingSize, out AppendIndexOut )atomically execute (  if( ( (dest64.AppendIndex+1) % RingSize ) != ( dest64.PopFrontIndex % RingSize) )  {   AppendIndexOut =dest64.AppendIndex;   ++dest64.AppendIndex;  }  else  {   AppendIndexOut= 0xffffffff; // error, ring-buffer full  } )b. Ring Buffer PopFront

InterlockedPopFront( in dest64, in RingSize, out PopIndexOut )atomically execute (  if( ( (dest64.PopFrontIndex) % RingSize ) != ( dest64.AppendIndex % RingSize) )  {   PopIndexOut =dest64.PopFrontIndex;   ++dest64.PopFrontIndex;  }  else  {  PopIndexOut = 0xffffffff; // error ring buffer empty  } )c. Example Use Cases

-   -   i. Initialize the ringbuffer with available number of entries        using InterlockedAppend    -   ii. A number of threads run and temporarily pick/allocate        entries using InterlockedPopFront    -   iii. Entries get returned to the ring buffer using        InterlockedAppend    -   iv. Threads can decide not to wait for entries and deal with        this case Pseudo code for a multi-producer sample and a        multi-consumer sample are illustrated in FIGS. 84-85 .

A producers pseudo code sample is illustrated in FIG. 84A. For thisexample, assume the job_entry_ready_buffer is initialized to all zerosand the job_entry_consumed_buffer is initialized to all 1s:

A consumer pseudo code sample is illustrated in FIG. 84B. For thisexample, it is assumed that the job_entry_ready_buffer is initialized toall zeros and the job_entry_consumed_buffer is initialized to all 1s.

FIG. 83A illustrates an example ring buffer implemented in accordancewith one embodiment. A ring buffer pop back operation is shown in whichentries N, N+1, etc., are popped and stored in ring buffer entries 0, 1,etc. FIG. 83B illustrates a 64-bit destination register 8211 storing theappend index value 8212 and the pop front index value 8213, inaccordance with the following code sequence:

InterlockedPopBack( in dest64, in RingSize, out PopIndexOut ) atomicallyexecute ( if( ( (dest64.PopFrontIndex) % RingSize ) != (dest64.AppendIndex % RingSize) ) {  PopIndexOut = dest64.AppendIndex; -- dest64.AppendIndex; } else {   PopIndexOut = 0xffffffff; // errorring buffer empty }   )

V. Atomic Multiplication Operations

One embodiment of a multiply atomic specifies a destination and a typevalue. By way of example, a multiply atomic may take the form:

-   -   InterlockedMultiply(destination, type value)

In one embodiment, the multiply operation atomically multiplies a valueof a specified data type with the value in the destination, which may bethe same data type or a different data type.

Multiply atomic values may be, by way of example and not limitation,4-bit, 8-bit, 16-bit, 32-bit, and 64-bit integers and 16-bit, 32-bit,and 64-bit floating point values. The values may be signed or unsigned.Moreover, a number of parallel multiply operations may be performedbased on the smallest data element size. For example, floating pointmultiplication circuitry may be configured to perform a single 32-bitfloating point multiplication or dual 16-bit floating pointmultiplications. Formats such as Bfloat16 or TensorFloat16 may be usedto efficiently perform the parallel multiplications. Similarly, aninteger multiplier may be capable of performing a single 32-bitmultiplication, dual 16-bit multiplications, four 8-bit multiplications,or eight 4-biut multiplications. Various other types of data formats andparallel operations may be used while still complying with theunderlying principles of the invention including, for example, 2×FP16,float2, 4×FP16, 11_11_10FP and 2×11_11_10FP.

These atomics may be used for a variety of purposes including machinelearning operations, Weighted Blended Order Independent Transparency(OIT) or Opacity Shadow Maps.

Apparatus and Method for Graphics Processor-Managed Tiled Resources

One embodiment of the invention improves the efficiency with which auser-written GPU program can cache and reuse data stored in a buffer ortexture. This embodiment also provides for a logical representation oflarge, procedurally-computed resources that may or may not physicallyfit into the GPU memory at the same time.

In one embodiment of the invention, a new tiled resource is defined andmanaged by the GPU, referred to herein as a GPU managed tiled resourceor a GPU managed buffer. In one implementation, the buffer or othertiled storage resource contains up to N fixed sized blocks of memory.Different GPU architectures may support a different maximum number ofblocks (N).

In one embodiment, the GPU-managed tiled resource is used to efficientlyshare data between shaders—i.e., where one shader acts as a “producer”for one or more “consumer” shaders. For example, the producer shader maygenerate procedurally-updated content which the consumer shader may usewithout involving interaction with the CPU. As another example, in raytracing implementations, various forms of skinning animation may need tobe updated on traversal. One shader may skin a small portion of themesh, storing results in the tiled resource, without CPU intervention.As other rays trace the same portion, they can access the data locallyfrom the tiled resource, without accessing main memory.

FIG. 85A illustrates one embodiment of an architecture for implementingGPU-managed tiled resources 8531. A graphics processor 8521 includes ascheduler 8510 for scheduling shaders 8511A-B on the set of executionunits 4001. Execution of the shaders requires access to tiled resources8531 which are managed by a resource manager 8512. In the exampleprovided below, one shader 8511A is designated as a “producer”, storingits results in the tiled resource 8531, and the other shader 8511B is a“consumer,” using the results generated by the producer shader 8511A.Consequently, the producer shader 8511A needs access to write to thetiled resource 8521 and the consumer shader 8511B needs read access tothe tiled resource 8531. It should be noted, however, that aproducer/consumer architecture is not required for complying with theunderlying principles of the invention.

In one implementation, the tiled resource 8531 comprises an on-chip tilememory or tile buffer, which stores tile-sized blocks 0-(N−1) of data.The “tile” size may be variable based on the architecture of thegraphics processor 8521 and the configuration of the graphics processingpipeline. In one embodiment, the graphics processing pipeline isconfigured to perform tile-based deferred rendering, tile-basedimmediate mode rendering, and/or other form of tile-based graphicsprocessing, using the tiled resource 8531.

In one embodiment, an execution unit (EU) 4001 or other processing unit,requests a block using a hash value or other form of ID 8501 (e.g., a64-bit hash in one embodiment). A resource manager 8512 determineswhether the block exists within the tiled resource 8531 comprising Nfixed sized blocks. If no such block is found, the buffer manager 8510evicts the least recently used (LRU) block or selects an unused block ifone exists. The response 8502 identifies the allocated block which thebuffer manager 8510 marks as “used” with the given hash value. In oneimplementation, a flag is also returned indicating that the block isnew. A least recently used block which is replaced loses the old contentwhich it stored. If the block is already there, a flag is returned thatindicates the block already exists and it is returned nevertheless.

While illustrated as a component within the graphics processor 8521, thetiled resource 8531 may be implemented within a memory external to thegraphics processor 8521 such as a system memory or system-level cache.

Certain classes of shaders 8511A-B which execute on the EUs 4001 of aGPU are a priori known to require a block of memory. For example, theseshaders may always execute in the lanes of a wave. In one embodiment,the scheduler 8510 which schedules the execution of these shaders8511A-B constructs a 64 bit ID/Hash from system-generated values. Forexample, one embodiment, in the context of raytracing, uses theInstanceID and the GeometryID to construct a unique 64-bit hash.However, a variety of other system generated variables may be used.

In this embodiment, the scheduler 8510 checks via the resource manager8512 whether there is already a block of the tiled resource 8531allocated for the 64 bit hash. If so, the shader 8511A-B is executedunder the assumption that the block already contains cached data andthat this can be consumed by the shader and the shader is scheduled onthe EUs 4001. The resource manager 8512 locks the block of memory frombeing reused as long as the shader that uses the data cached lockin thatblock is executing. As the shader is executed by one or more EUs 4001,it updates the block in the tiled resource 8531 using the block ID 8501and, for certain operations, receives responses 8502 from the resourcemanager 8512.

In one embodiment, if the scheduler 8510 initially finds that there isno block with the given 64-bit hash, the resource manager 8512 locatesan unused block or uses the least recently used block (or other block)that has already allocated and isn't currently in use. If it cannotlocate such a block, it may postpone execution of the shader until sucha block becomes available. When one is available, the tiled resourcemanager 8512 locks the tiled resource block from being reused as long asthe shader is executing and schedules the shader. A flag may be passedto the shader to indicate that the block is empty and that the shadercan use it to generate and store data. After writing data to the tiledresource block, the shader may continue execution as if the tiledresource block with its data had already been available.

Returning to the consumer/producer example above, a producer shader8511A may be scheduled to generate a novel block or tile of theprocedural resource 8531 if the requested hash is not valid in the pool.Such a requested hash may be generated by one or more consumer shaders8511B, which the resource manager 8512 would block until their requestis filled.

In one embodiment, tiled resource blocks are evicted to a solid-statedevice 8515 or other high speed storage medium. The SSD 8515 or otherstorage device may be integrated locally on the same substrate and/orcard as the graphics processor 8521 and may be configured to save tiledresource blocks and other data during internal graphics processor 8521context switches.

A method in accordance with one embodiment is illustrated in FIG. 85B.The method may be implemented within the context of the architecturesdescribed above, but is not limited to any particular architecture.

At 8551 the scheduler evaluates the next shader to be scheduled forexecution and, at 8552, determines a hash ID to be used to identify thetiled resource block (e.g., using one or more of the techniquesdescribed herein). At 8553, the scheduler queries the tiled resourcemanager with the hash ID.

If a block is already allocated for this hash ID, determined at 8554,then the tiled resource manager locks the tiled resource block at 8555and the shader uses the tiled resource block during execution at 8556.The tiled resource block may subsequently be unlocked when the shadercompletes, unless it is locked with a hash ID of a consumer shader thatwill require the data after the current (producer) shader completes. Inany case, the process returns to 8551 for scheduling of the next shader.

If, at 8554, no tiled resource block is identified with the hash ID,then the tiled resource manager assigns a tiled resource block to thehash ID and may allocate a flag to the shader indicating that it may usethis tiled resource block. As mentioned, the tiled resource manager mayevict existing data from a tiled resource block to assign the tiledresource block to the current shader. The tiled resource block is lockedat 855 and the shader uses the tiled resource block during execution at8556.

The GPU-managed tiled buffer 8531 may be used in a variety of ways. Forexample, a SIMD wave of lanes want to enter the same intersection shaderbox bundled by bindless tread dispatcher (described below). Before theintersection shader is run, the hardware requests a block from thebuffer manager 8510.

The 64-bit hash may be generated in different ways. For example, in oneembodiment, the 64-bit hash is the InstanceID of the current raytraversal instance combined with the frame counter. If the block is new,the hardware may launch a user compute shader running within the lanesof the wave that then fills the block (e.g., with skinned triangles). Ifthe block is old, then the shader may not be launched. An intersectionshader is then executed and is provided with the pointer to the block.The intersection shader may then perform ray/triangle intersectionsand/or support may be provided for a hardware instruction for theray/triangle intersections (as described herein). Alternatively, theblock may be designed to only contain triangles. In this case, thehardware iterates over these triangles (without building a BVH overthem) and may, for example, update closest hit shaders or call intoany-hit shaders. Various other use cases may take advantage of theGPU-managed tiled resource 8531 as described above.

Apparatus and Method for Efficient Lazy BVH Build

Complex dynamic scenes are challenging for real-time ray tracingimplementations. Procedural surfaces, skinning animations, etc., requireupdates of triangulation and accelerating structures in each frame, evenbefore the first ray is launched.

Lazy builds evaluate scene elements “on-demand”, as driven by raytraversal. The rendering of a frame starts with a coarse accelerationstructure like a scene-graph or hierarchies of the previous frame, thenprogressively builds the newly required acceleration structures for theobjects that are hit by rays during traversal. Invisible objects can beeffectively excluded from the construction process. However, thesetechniques are not easily implemented with current systems and APIs,because the higher-level (i.e., per-object) programmability essentialfor computing the instance visibility is not supported.

One embodiment of the invention supports a multi-pass lazy build (MPLB)for real-time ray tracing that resolves these problems with an extendedprogramming model. It allows the instance-level traversal to be trackedduring each ray dispatch and selectively builds bottom levelacceleration structures (BLASs) for only the potentially visiblegeometry at render time. Akin to some adaptive sampling techniques, MPLBas described herein may require multiple ray dispatches over the sameset of pixels to relaunch rays to previously unbuilt parts of the scene,but certain embodiments of the invention include techniques to minimizethis overhead, such as the assumption of frame-to-frame coherence andrasterized primary visibility. These techniques can provide asignificant reduction in build complexity compared to one-time builderswith only a marginal increase in traversal cost on average.

FIG. 86A illustrates one embodiment of an on-demand (or “lazy”) builder8607 for performing lazy build operations as described herein. Inaddition, this embodiment includes traversal suspension circuitry/logic8620 for suspending ray traversal. The ray traversal suspensioncircuitry/logic 8620 may be implemented in hardware, software, or anycombination thereof. Ray stack storage 8605 stores suspended ray stacks8610 when traversal is suspended (as described in greater detailherein). In addition, GPU-side command scheduling launches lazy buildtasks and ray continuations on execution units 4001 without supervisionby the CPU. Traversal atomics are also used to reduce shader overhead.

Traversal Suspension upon Missing Bottom Level Acceleration Structure(BLAS) Encounter

In one implementation, using a programming model with a traversal shaderextension, missing instances (e.g., missing bottom level accelerationstructures of the BVH 8005) are programmatically marked, so that theycan be identified and updated in a separate pass. Then either anincomplete traversal is performed or traversal is aborted.

To render the final pixels, the primary shader of the correspondingpixel may need to be relaunched, leading to several repeated traversaland shader execution operations. In one embodiment, traversal suspensionlogic 8620 backs up the entire ray context 8610 (ray stack,continuations, etc.) into off-chip memory 8605 when traversal issuspended. In one embodiment, this traversal suspension an intrinsicfunction which is managed by the driver (e.g., SuspendTraversal( ));however, the underlying principles of the invention are not limited tothis implementation. In addition, a new DispatchRay( ) variant in thehost side—executed by the CPU 3199—re-schedules the suspended ray stacksfrom the ray context 8610 to continue traversal shader execution.

GPU-side Command Scheduling for Build and Dispatch

Another significant overhead of current lazy build implementations isthe continuous requirement of CPU 3199 readback and conditionalscheduling of the BVH builder 8007 and ray dispatching on the GPU 2505.To improve efficiency, in one implementation, the BVH processingcircuitry/logic 8004 runs the BVH build asynchronously with the raytraversal 8003. Upon the completion of the build tasks, the ray tracingengine 8000 executes the ray dispatch to continue the suspended raystacks from the ray context 8610.

Traversal Atomics to Reduce Traversal Shader Overhead

One problem with current implementations is that if an instance ismissing (unbuilt), several rays may traverse it, and mark it for thelazy builder 8607 to update it. A simple task that could be done by justone traversal shader invocation is repeated by hundreds or moreinvocations. The traversal shader is not resource-intensive, but it hasa significant overhead to launch, perform input/output functions, andstore results.

In one embodiment of the invention, unbuilt instance leaves can bemarked as “atomic” nodes. Atomic nodes can be traversed by only one rayat once. An atomic node is locked once a ray traverses it, and unlockedat the end of the traversal shader execution. In one embodiment, thetraversal shader sets the status of a node to “invalid”, which preventsrays entering it even after the lock is released. This allows thetraversal hardware to either skip the node, or suspend the traversal ofthe ray, without executing a new traversal shader.

In one embodiment, for atomics nodes, instead of regular atomicsemantics, certain mutex/condition semantics are used. For example, ifthe traversal circuitry/logic 8003 traverses a ray to a proxy node, itattempts to lock the node. If this fails as the node is already locked,it automatically executes “suspendRay” without returning to the EU 4001.If the locking is executed successfully, the traversal circuitry/logic8003 processes the proxy node.

Lazy Build of Acceleration Structures with a Traversal Shader

One embodiment of the invention operates in accordance with theprocessing flow shown in FIG. 86B. By way of an overview, the on-demandbuilder 8607 builds acceleration structures over geometry instances 8660determined to be potentially visible. The potentially visible instances8660 are generated by a pre-builder 8655 based on primary visibilitydata from the G-buffer 8650 and visibility history data 8651 indicatingvisibility in the previous frame. The potentially visible instances 8660may also be determined based on a visible bottom level accelerationstructure (BLAS) map 8675 which indicates the bottom level nodes of theacceleration structure that include visible primitives. In oneembodiment, the visible BLAS map 8675 is continually updated in responseto traversal operations performed by the traversal logic 8670, which mayinclude dedicated traversal circuitry and/or traversal shaders executedon the execution units of the graphics processor.

The on-demand builder 8607 generates those portions of the accelerationstructure which are associated with the potentially visible instances8660. A ray generation shader 8678 selectively generates rays based onthese portions of the acceleration structure which the traversal unit8670 traverses through the acceleration structure portions. Thetraversal unit 8670 notifies the on-demand builder 8670 of additionalacceleration structure nodes which it requires for traversal and updatesthe BLAS pixel masks 8677 used by the ray generation shader 8678 (e.g.,which only generates rays for unmasked pixels) and visible BLAS map8675.

Thus, the on-demand builder 8706 selectively builds bottom levelacceleration structures over the potentially visible instances 8660 andthe instance visibility is updated during ray traversal 8670. Unlike theprevious implementations, the embodiments of the invention operate inmultiple passes in order to avoid complicated ray scheduling. The ideais analogous to recent texture-space shading approaches wherevisibility-driven marking of texels is used to avoid redundant shadingbefore the final rendering.

In operation, the BLASes for empty instances that were marked aspotentially visible in the previous pass are first built. In the secondpass, the ray generation shader 8678 selectively reshoots the rays tothe unfinished pixels, where a traversal shader is used to either recordmore potentially visible empty instances or complete the pixel. Thenumber of incomplete pixels decreases after each iteration until thereare no rays left that traversed an empty instance.

One embodiment of the invention performs a hybrid rendering using theGPU rasterizer and ray tracing hardware together. This is because whencreating the G-buffer 8650, the primary visibility of all instances inthe scene is easily obtained. Hence, the pre-builder 8655 in theseembodiments takes advantage of hybrid rendering by efficientlyconstructing the initial acceleration structure using this data. Beforethe first iteration, potentially visible instances 8660 are marked inthis pre-build heuristic (as discussed below).

The code sequence below is an abstracted high level shader language(HLSL) describing one embodiment of the traversal shader described withsome intrinsic- and user-functions:

RWStructuredBuffer <vblas> visibleBlasMap[ ] : register (u∅, space∅);RWStructuredBuffer <pmask> pixelMasks[ ] : register (u∅, space1);[shader (“traversal”)] void myVisibilityShader (in RayPayload rp) { uint2 index = DispatchRaysIndex( );  uint2 size =DispatchRaysDimensions( );  UpdateVisibility (visibleBlasMap,InstanceID( ), true);  // Control BLAS traversal with updating pixelmask  RaytracingAccelerationStructure myAccStructure:  boolisInstanceEmpty = IsEmptyInstance( );  if (isInstanceEmpty) {  UpdateMask (pixelMasks, index.y*size.x + index.x, false);  rp.trav_valid = false;   skipTraversal( );  }  else if(!isInstanceEmpty && !rp.trav_valid)   skipTraversal( );  else {  myAccStructure = FetchBLAS(InstanceID( ) );   RayDesc transformedRay ={ . . . };   // Set the next level instance and hit shader table offset  SetInstance (myAccStructure, transformedRay, hitShaderOffset);  } }

The SkipTraversal( ) intrinsic is defined to ignore the current instanceand continue traversal in the higher-level acceleration structure. Asmentioned, the visible bottom-level acceleration structure (BLAS) map8675 is used to record instance visibility commonly used in accelerationstructure builders and traversal shaders. As shown in FIG. 86C, oneembodiment of the visible BLAS map 8675 contains a flag 8676 associatedwith each BLAS ID 8674 indicating the BLAS visibility to which theinstance refers and two flags, Built_Full and Built_Empty, indicatingwhether the BLAS has already been built. In addition, a boolean flag,trav_valid, is added to the ray payload to keep track of traversalstatus, which can be used for checking whether the ray has encounteredan empty instance thus far.

In one embodiment, the visibility in the traversal shader isconservatively updated because all traversed instances are potentiallyvisible to the current ray. Hence, the first task is to set thevisibility flag as True for the corresponding BLAS of the currentinstance. It also sets the visibility history (vis_history) flag as Trueto reuse it in the next frame (line 9 of the above code sequence). Next,the traversal destination is determined based on the status of thecurrent instance (empty or full) and the ray status (i.e., thetrav_valid value). This is classified into three states 8690-8692 asshown in FIG. 86D.

For an empty instance 8690, the corresponding pixel mask is reset (line15) for reshooting rays in the next pass. The current traversal is theninvalidated by setting the trav_valid flag in the ray payload (line 16).Finally, TLAS traversal continues by invoking SkipTraversal( ).

For the full instance and invalid traversal case 8691, the currentinstance has a built BLAS, but the ray has encountered an empty instanceso far (i.e., trav_valid is False). Because the ray will be eventuallyshot again to the current pixel, the BLAS traversal can be skipped (line20).

For a full instance and valid traversal 8692, since the ray normallytraversed the acceleration structure without empty instances, thetraversal shader fetches the BLAS of the current instance and continuesthe traversal. If the ray maintains validity until the end of thetraversal, the ray will normally invoke and execute the closest-hit ormiss shader.

Otherwise, those shaders return control without executing their code andfinish the current pass, which prevents the overheads of hardware raytraversal and shader launching for secondary rays. In the next pass, therays are shot again only to the pixel having the “False” mask, and avalid traversal for those pixels is attempted.

For the acceleration structure building operation, the BLASes of theinstances are built or empty instances are created, depending on thevisibility flag of the visibility bit mask. The potentially visibleinstance normally constructs the BLAS (BUILD_FULL), and the invisibleinstance computes only the bounding box of the geometry and packs it inthe leaf node of TLAS (BUILD_EMPTY). The other two flags are alsoreferred to, indicating whether a BUILD_FULL or BUILD_EMPTY action wasalready performed for the current object in the previous pass. Bychecking these flags, duplicate actions can be avoided for the sameobject in the different iterations of the Build-Traverse loop.

Once the BLAS build process for the objects is finished, the finalacceleration structure is constructed by building the TLAS over theseBLASes. The TLAS is rebuilt only in the first pass and refitted in therest of the passes because the bounding boxes of all objects could bealready set up in the first pass.

As described above, one embodiment of the invention performs multiplepasses, which makes it sometimes redundantly shoot rays for the samepixel. This is because the current pass should make up for the invalidtraversal in the previous pass. This can lead to redundant hardware raytraversal and shader invocations. However, one embodiment limits thisoverhead of the traversal costs only to the pixels corresponding toinvalid traversal by applying a pixel mask.

Moreover, different techniques are used to identify potentially visibleBLASes (and build them), even before the first ray is traversed (e.g.,by the pre-builder 8655). Using the G-buffer 8650, directly visibleinstances that are likely to be traversed by primary rays can be marked.Furthermore, there is assumed to be a significant amount offrame-to-frame coherence; thus, the BLASes of instances traversed in theprevious frame are also pre-built. The combination of these twotechniques greatly reduces the number of Build-Traverse iterations.

Apparatus and Method for a Material Culling Mask

Existing ray tracing APIs use an 8-bit cull mask to skip ray traversalfor certain geometry instances. This is used, for example, to preventspecific objects from casting shadows, or to hide objects fromreflections. This feature allows different subsets of geometry to berepresented within a single acceleration structure as opposed tobuilding separate acceleration structures for each subset. The bitsettings in the 8-bit mask can be used to balance traversal performanceand the resource overhead for maintaining multiple accelerationstructures. For example, if a bit in the mask is set to 0, thecorresponding instance may be ignored.

Rendering engines can associate multiple geometry instances with anasset and each geometry instance can contain multiple materials.However, current ray tracing APIs only allow specification of theculling mask at the granularity of an instance. This means that assetswhich have different culling masks on different materials cannot usestandard culling. As a workaround, current implementations use any-hitshaders to ignore intersections, which is expensive and complicated.

As illustrated in FIG. 87 , one embodiment of the invention exposesthese masking controls on a per-material basis. In particular, oneimplementation includes an N-bit material-based cull mask 8701 to skipray traversal for portions of geometry instances associated with certainmaterials. In one embodiment, an 8-bit material-based cull mask is used,but the underlying principles of the invention are not limited to thisimplementation. In contrast to existing implementations, thematerial-based cull mask 8701 is exposed and can be utilized by thetraversal circuitry/logic 8003 for instance culling on a per-materialbasis as well as a per-instance basis.

In one specific implementation, the N-bit cull mask 8701 is storedinside of a hit group 8700, providing fixed-function per-materialculling and alleviating the need for expensive any-hit shaderworkarounds. A “hit group” 8700 as used herein is an API object thatcontains a set of shaders used to process rays hitting a given object inthe scene. The set of shaders may include, for example, a closest-hitshader, an any-hit shader, and (for procedural geometry) an intersectionshader. In one implementation, the material-based cull mask 8701 isassociated with the hit group 8700, as an additional piece of data.

To associate the cull mask 8701 with the hit group 8700, the cull mask8701 may be stored within the 32-byte shader record that the APIprovides for the implementation to use (e.g., identified via a record IDas described herein). Note, however, that the underlying principles ofthe invention are not limited to any particular technique forassociating a cull mask with a hit group.

In one embodiment, the traversal/intersection circuitry 8003 directlyculls potential hits based on the material-based cull mask 8701. Forexample, a mask value of 0 may indicate that instances with acorresponding material should be culled. Alternatively, or in addition,this behavior can be emulated by injecting any-hit shaders inside thedriver.

Geometric Image Accelerator and Method

A geometry image is a mapping of a three dimensional (3D) triangle meshonto a two dimensional (2D) domain. In particular, a geometry image mayrepresent geometry as a 2D array of quantized points. Correspondingimage data such as colors and normals may also be stored in 2D arraysusing the same implicit surface parametrization. The 2D triangle meshrepresented by the 2D array is defined by a regular grid of vertexpositions with implicit connectivity.

In one embodiment of the invention, a geometry image is formed bymapping a 3D triangle mesh into a 2D plane, resulting in an impliedtriangle connectivity defined by a regular grid of vertex positions. Theresulting 2D geometry image can be processed in various ways within thegraphics pipeline including down-sampling and up-sampling using mipmaps.

As illustrated in FIG. 88 , one embodiment of the invention performs raytracing by generating a quadtree structure 8850 over the geometry imagedomain, where each quadtree node 8800, 8810-8813 stores an axis-alignedbounding box (AABB) over the vertex positions of the 2D triangle mesh8820. As illustrated, each node 8800, 8810-8813 stores the minimum andmaximum coordinates of the associated AABB which contains one or more ofthe triangles and/or vertices. This results in a structure which isextremely regularized and very easy to compute.

Once the AABBs are constructed over the 2D triangle mesh, ray tracingoperations may be performed using the AABBs as described herein withrespect to the various embodiments of the invention. For example,traversal operations may be performed to determine that a ray traversesone of the bottom-level nodes 8810-8813 of the BVH. The ray may then betested for intersections with the 2D mesh and hit results (if any)generated and processed as described herein (e.g., in accordance with amaterial associated with the 2D triangle mesh).

As illustrated, in one embodiment, storage/compression logic 8850 isconfigured to compress and/or store the AABBs as dual image pyramids8855, one storing the minimum values and one storing the maximum values.In this embodiment, different compression schemes developed for geometryimages can be used to compress the minimum and maximum image pyramids.

The quadtree structures 8850, 8860-8861 described above with respect toFIG. 88 may be generated by the BVH builder 8007. Alternatively, thequadtree structures may be generated by a different set of circuitryand/or logic.

Apparatus and Method for Box-Box Testing and Accelerated CollisionDetection for Ray Tracing

FIG. 89A-B illustrate a ray tracing architecture in accordance with oneembodiment of the invention. A plurality of execution units 8910 executeshaders and other program code related to ray tracing operations. A“Traceray” function executed on one of the execution units (EUs) 8910triggers a ray state initializer 8920 to initialize the state requiredto trace a current ray (identified via a ray ID/descriptor) through abounding volume hierarchy (BVH) (e.g., stored in a in a stack 5121 in amemory buffer 8918 or other data structure in local or system memory3198).

In one embodiment, if the Traceray function identifies a ray for which aprior traversal operation was partially completed, then the stateinitializer 8920 uses the unique ray ID to load the associated raytracing data 4902 and/or stacks 5121 from one or more buffers 8918 inmemory 3198. As mentioned, the memory 3198 may be an on-chip/localmemory or cache and/or a system-level memory device.

As discussed with respect to other embodiments, a tracking array 5249may be maintained to store the traversal progress for each ray. If thecurrent ray has partially traversed a BVH, then the state initializer8920 may use the tracking array 5249 to determine the BVH level/node atwhich to restart.

A traversal and raybox testing unit 8930 traverses the ray through theBVH. When a primitive has been identified within a leaf node of the BVH,instance/quad intersection tester 8940 tests the ray for intersectionwith the primitive (e.g., one or more primitive quads), retrieving anassociated ray/shader record from a ray tracing cache 8960 integratedwithin the cache hierarchy of the graphics processor (shown here coupledto an L1 cache 8970). The instance/quad intersection tester 8940 issometimes referred to herein simply as an intersection unit (e.g.,intersection unit 5103 in FIG. 51 ).

The ray/shader record is provided to a thread dispatcher 8950, whichdispatches new threads to the execution units 8910 using, at least inpart, the bindless thread dispatching techniques described herein. Inone embodiment, the ray/box traversal unit 8930 includes thetraversal/stack tracking logic 5248 described above, which tracks andstores traversal progress for each ray within the tracking array 5249.

A class of problems in rendering can be mapped to test box collisionswith other bounding volumes or boxes (e.g., due to overlap). Such boxqueries can be used to enumerate geometry inside a query bounding boxfor various applications. For example, box queries can be used tocollect photons during photon mapping, enumerate all light sources thatmay influence a query point (or query region), and/or to search for theclosest surface point to some query point. In one embodiment, the boxqueries operate on the same BVH structure as the ray queries; thus theuser can trace rays through some scene, and perform box queries on thesame scene.

In one embodiment of the invention, box queries are treated similarly toray queries with respect to ray tracing hardware/software, with theray/box traversal unit 8930 performing traversal using box/boxoperations rather than ray/box operations. In one embodiment, thetraversal unit 8930 can use the same set of features for box/boxoperations as used for ray/box operations including, but not limited to,motion blur, masks, flags, closest hit shaders, any hit shaders, missshaders, and traversal shaders. One embodiment of the invention adds abit to each ray tracing message or instruction (e.g., TraceRay asdescribed herein) to indicate that the message/instruction is associatedwith a BoxQuery operation. In one implementation, BoxQuery is enabled inboth synchronous and asynchronous ray tracing modes (e.g., usingstandard dispatch and bindless thread dispatch operations,respectively).

In one embodiment, once set to the BoxQuery mode via the bit, the raytracing hardware/software (e.g., traversal unit 8930, instance/quadintersection tester 8940, etc) interprets the data associated with theray tracing message/instruction as box data (e.g., min/max values inthree dimensions). In one embodiment, traversal acceleration structuresare generated and maintained as previously described, but a Box isinitialized in place of a Ray for each primary StackID.

In one embodiment, hardware instancing is not performed for box queries.However, instancing may be emulated in software using traversal shaders.Thus, when an instance node is reached during a box query, the hardwaremay process the instance node as a procedural node. As the header ofboth structures is the same, this means that the hardware will invokethe shader stored in the header of the instance node, which can thencontinue the point query inside the instance.

In one embodiment, a ray flag is set to indicate that the instance/quadintersection tester 8940 will accept the first hit and end the search(e.g., ACCEPT_FIRST_HIT_AND_END_SEARCH flag). When this ray flag is notset, the intersected children are entered front to back according totheir distance to the query box, similar to ray queries. When searchingfor the closest geometry to some point, this traversal ordersignificantly improves performance, as is the case with ray queries.

One embodiment of the invention filters out false positive hits usingany hit shaders. For example, while hardware may not perform an accuratebox/triangle test at the leaf level, it will conservatively report alltriangles of a hit leaf node. Further, when the search box is shrunkenby an any hit shader, hardware may return primitives of a popped leafnode as a hit, even though the leaf node box may no longer overlap theshrunken query box.

As indicated in FIG. 89A, a box query may be issued by the executionunit (EU) 8910 sending a message/command to the hardware (i.e.,Traceray). Processing then proceeds as described above—i.e., through thestate initializer 8920, the ray/box traversal logic 8930, theinstance/quad intersection tester 8940, and the bindless threaddispatcher 8950.

In one embodiment, the box query re-uses the MemRay data layout as usedfor ray queries, by storing the lower bounds of the query box in thesame position as the ray origin, the upper bounds in the same positionas the ray direction, and a query radius into the far value.

struct MemBox {  // 32 Bytes (semantics changed)  Vec3f lower;  // thelower bounds of the query box  Vec3f upper;   // the upper bounds of thequery box  float unused;  float radius; // additional extension of thequery box (L0 norm)  // 32 Bytes (identical to standard MemRay) };

Using this MemBox layout, the hardware uses the box [lower-radius,upper+radius] to perform the query. Therefore, the stored bounds areextended in each dimension by some radius in L0 norm. This query radiuscan be useful to easily shrink the search area, e.g. for closest pointsearches.

As the MemBox layout just reuses the ray origin, ray direction, andT_(far) members of the MemRay layout, data management in hardware doesnot need to be altered for ray queries. Rather, the data is stored inthe internal storage (e.g., the ray tracing cache 8960 and L1 cache8970) like the ray data, and will just be interpreted differently forbox/box tests.

In one embodiment, the following operations are performed by theray/state initialization unit 8920 and ray/box traversal unit 8930. Theadditional bit “BoxQueryEnable” from the TraceRay Message is pipelinedin the state initializer 8920 (affecting its compaction acrossmessages), providing an indication of the BoxQueryEnable setting to eachray/box traversal unit 8930.

The ray/box traversal unit 8930 stores “BoxQueryEnable” with each ray,sending this bit as a tag with the initial Ray load request. When therequested Ray data is returned from the memory interface, withBoxQueryEnable set, reciprocal computation is bypassed and instead adifferent configuration is loaded for all components in the RayStore(i.e., in accordance with a box rather than a ray).

The ray/box traversal unit 8930 pipelines the BoxQueryEnable bit to theunderlying testing logic. In one embodiment, the raybox data path ismodified in accordance with the following configuration settings. IfBoxQueryEnable==1, the box's plane is not changed as it is change basedon the sign of the x, y and z components of the ray's direction. Checksperformed for the ray which are unnecessary for the raybox are bypassed.For example, it is assumed that the querying box has no INF or NANs sothese checks are bypassed in the data path.

In one embodiment, before processing by the hit-determination logic,another add operation is performed to determine the value lower+radius(basically the t-value from the hit) and upper−radius. In addition, uponhitting an “Instance Node” (in a hardware instancing implementation), itdoes not compute any transformation but instead launches an intersectionshader using a shader ID in the instance node.

In one embodiment, when BoxQueryEnable is set, the ray/box traversalunit 8930 does not perform the NULL shader lookup for any hit shader. Inaddition, when BoxQueryEnable is set, when a valid node is of the QUAD,MESHLET type, the ray/box traversal unit 8930 invokes an intersectionshader just as it would invoke an ANY HIT SHADER after updating thepotential hit information in memory.

In one embodiment, a separate set of the various components illustratedin FIG. 89A are provided in each multi-core group 3100A (e.g., withinthe ray tracing cores 3150). In this implementation, each multi-coregroup 3100A can operate in parallel on a different set of ray dataand/or box data to perform traversal and intersection operations asdescribed herein.

Apparatus and Method for Meshlet Compression and Decompression for RayTracing

As described above, a “meshlet” is a subset of a mesh created throughgeometry partitioning which includes some number of vertices (e.g., 16,32, 64, 256, etc) based on the number of associated attributes. Meshletsmay be designed to share as many vertices as possible to allow forvertex re-use during rendering. This partitioning may be pre-computed toavoid runtime processing or may be performed dynamically at runtime eachtime a mesh is drawn.

One embodiment of the invention performs meshlet compression to reducethe storage requirements for the bottom level acceleration structures(BLASs). This embodiment takes advantage of the fact that a meshetrepresents a small piece of a larger mesh with similar vertices, toallow efficient compression within a 128B block of data. Note, however,that the underlying principles of the invention are not limited to anyparticular block size.

Meshlet compression may be performed at the time the correspondingbounding volume hierarchy (BVH) is built and decompressed at the BVHconsumption point (e.g., by the ray tracing hardware block). In certainembodiments described below, meshlet decompression is performed betweenthe L1 cache (sometimes “LSC Unit”) and the ray tracing cache (sometimes“RTC Unit”). As described herein, the ray tracing cache is a high speedlocal cache used by the ray traversal/intersection hardware.

In one embodiment, meshlet compression is accelerated in hardware. Forexample, if the execution unit (EU) path supports decompression (e.g.,potentially to support traversal shader execution), meshletdecompression may be integrated in the common path out of the L1 cache.

In one embodiment, a message is used to initiate meshlet compression to128B blocks in memory. For example, a 4×64B message input may becompressed to a 128B block output to the shader. In this implementation,an additional node type is added in the BVH to indicate association witha compressed meshlet.

FIG. 89B illustrates one particular implementation for meshletcompression including a meshlet compression block (RTMC) 9030 and ameshlet decompression block (RTMD) 9090 integrated within the raytracing cluster. Meshlet compression 9030 is invoked when a new messageis transmitted from an execution unit 8910 executing a shader to the raytracing cluster (e.g., within a ray tracing core 3150). In oneembodiment, the message includes four 64B phases and a 128B writeaddress. The message from the EU 8910 instructs the meshlet compressionblock 9030 where to locate the vertices and related meshet data in localmemory 3198 (and/or system memory depending on the implementation). Themeshlet compression block 9030 then performs meshlet compression asdescribed herein. The compressed meshlet data may then be stored in thelocal memory 3198 and/or ray tracing cache 8960 via the memory interface9095 and accessed by the instance/quad intersection tester 8940 and/or atraversal/intersection shader.

In FIG. 89B, meshlet gather and decompression block 9090 may gather thecompressed data for a meshlet and decompress the data into multiple 64Bblocks. In one implementation, only decompressed meshlet data is storedwithin the L1 cache 8970. In one embodiment, meshlet decompression isactivated while fetching the BVH node data based on the node-type (e.g.,leaf node, compressed) and primitive-ID. The traversal shader can alsoaccess the compressed meshlet using the same semantics as the rest ofthe ray tracing implementation.

In one embodiment, the meshlet compression block 9030 accepts an arrayof input triangles from an EU 8910 and produces a compressed 128Bmeshlet leaf structure. A pair of consecutive triangles in thisstructure form a quad. In one implementation, the EU message includes upto 14 vertices and triangles as indicated in the code sequence below.The compressed meshlet is written to memory via memory interface 9095 atthe address provided in the message.

In one embodiment, the shader computes the bit-budget for the set ofmeshlets and therefore the address is provided such that footprintcompression is possible. These messages are initiated only forcompressible meshlets.

struct CompressMeshletMsg {  uint64_t   address;   // Header: 128Baligned destination address for the meshlet    float  vert_x[14];  // upto 14 vertex coordinates  uint32_t   vert_x_bits;   // max vertex bits uint32_t   numPrims;    // Number of triangles (always even for quads)    float vert_y[14];      uint32_t   vert_y_bits;    // max vertex bits uint32_t   numIdx;    // Number of indices  float  vert_z[14];     uint32_t   vert_z_bits;    // max vertex bits  uint32_t  numPrimIDBits;      int32_t   primID[14];    // primIDS  PrimLeafDescprimLeafDesc;  struct {   int8_t idx_x;   int8_t idx_y;   int8_t idx_z;  int8_t last; // 1 if triangle is last in leaf, 0 otherwise  }index[14];  // vertex indices  int32_t pad0;  int32_t pad1; }

In one embodiment, the meshlet decompression block 9090 decompresses twoconsecutive quads (128B) from a 128B meshlet and stores the decompresseddata in the L1 cache 8970. The tags in the L1 cache 8970 track the indexof each decompressed quad (including the triangle index) and the meshletaddress. The ray tracing cache 8960 as well as an EU 8910 can fetch a64B decompressed quad from the L1 cache 8970. In one embodiment, an EU8910 fetches a decompressed quad by issuing a MeshletQuadFetch messageto the L1 cache 8960 as shown below. Separate messages may be issued forfetching the first 32 bytes and the last 32 bytes of the quad.

Shaders can access triangle vertices from the quad structure as shownbelow. In one embodiment, the “if” statements are replaced by “sel”instructions.

   // Assuming vertex i is a constant determined by the compiler float3getVertexi(Quad& q, int triID, int vertexID) {  if (triID == 0)   returnquad.vi;  else if (i == j0)   return quad.v0;  else if (i == j1)  return quad.v1;  else if (i == j2)   return quad.v2; }

In one embodiment, The ray tracing cache 8960 can fetch a decompressedquad directly from the L1 cache 8970 bank by providing the meshletaddress and quad index.

GetQuadData {  uint1_t msb; // MS 32B or LS 32B  uint4_t triangle_idx;// index of the triangle inside the meshlet. always even for quads. uint64_t meshlet_addr; }

Meshlet Compression Process

After allocating bits for a fixed overhead such as geometric properties(e.g., flags and masks), data of the meshlet is added to the compressedblock while computing the remaining bit-budget based on deltas on(pos.x, pos.y, pos.z) compared to (base.x, base.y, base.z) where thebase values comprise the position of the first vertex in the list.Similarly prim-ID deltas may be computed as well. Since the delta iscompared to the first vertex, it is cheaper to decompress with lowlatency. The base position and primIDs are part of the constant overheadin the data structure along with the width of the delta bits. Forremaining vertices of an even number triangles, position deltas andprim-ID deltas are stored on different 64B blocks in order to pack themin parallel.

Using these techniques, the BVH build operation consumes lower bandwidthto memory upon writing out the compressed data via the memory interface9095. In addition, in one embodiment, storing the compressed meshlet inthe L3 cache allows for storage of more BVH data with the same L3 cachesize. In one working implementation, more than 50% meshlets arecompressed 2:1. While using a BVH with compressed meshlets, bandwidthsavings at the memory results in power savings.

Apparatus and Method for Bindless Thread Dispatching andWorkgroup/Thread Preemption in a Compute and Ray Tracing Pipeline

As described above, bindless thread dispatch (BTD) is a way of solvingthe SIMD divergence issue for Ray Tracing in implementations which donot support shared local memory (SLM) or memory barriers. Embodiments ofthe invention include support for generalized BTD which can be used toaddress SIMD divergence for various compute models. In one embodiment,any compute dispatch with a thread group barrier and SLM can spawn abindless child thread and all of the threads can be regrouped anddispatched via BTD to improve efficiency. In one implementation, onebindless child thread is permitted at a time per parent and theoriginating threads are permitted to share their SLM space with thebindless child threads. Both SLM and barriers are released only whenfinally converged parents terminate (i.e., perform EOTs). One particularembodiment allows for amplification within callable mode allowing treetraversal cases with more than one child being spawned.

FIG. 90 graphically illustrates an initial set of threads 9000 which maybe processed synchronously by the SIMD pipeline. For example, thethreads 9000 may be dispatched an executed synchronously as a workgroup.In this embodiment, however, the initial set of synchronous threads 9000may generate a plurality of diverging spawn threads 9001 which mayproduce other spawn threads 9011 within the asynchronous ray tracingarchitectures described herein. Eventually, converging spawn threads9021 return to the original set of threads 9000 which may then continuesynchronous execution, restoring the context as needed in accordancewith the tracking array 5249.

In one embodiment, a bindless thread dispatch (BTD) function supportsSIMD16 and SIMD32 modes, variable general purpose register (GPR) usage,shared local memory (SLM), and BTD barriers by persisting through theresumption of the parent thread following execution and completion(post-diverging and then converging spawn). One embodiment of theinvention includes a hardware-managed implementation to resume theparent threads and a software-managed dereference of the SLM and barrierresources.

In one embodiment of the invention, the following terms have thefollowing meanings:

Callable Mode: Threads that are spawned by bindless thread dispatch arein “Callable Mode.” These threads can access the inherited shared localmemory space and can optionally spawn a thread per thread in thecallable mode. In this mode, threads do not have access to theworkgroup-level barrier.

Workgroup (WG) Mode: When threads are executing in the same manner withconstituent SIMD lanes as dispatched by the standard thread dispatch,they are defined to be in the workgroup mode. In this mode, threads haveaccess to workgroup-level barriers as well as shared local memory. Inone embodiment, the thread dispatch is initiated in response to a“compute walker” command, which initiates a compute-only context.

Ordinary Spawn: Also referred to as regular spawn threads 9011 (FIG. 90), ordinary spawn are initiated whenever one callable invokes another.Such spawned threads are considered in the callable mode.

Diverging Spawn: As shown in FIG. 90 , diverging spawn threads 9001 aretriggered when a thread transitions from workgroup mode to callablemode. A divergent spawn's arguments are the SIMD width and fixedfunction thread ID (FFTID), which are subgroup-uniform.

Converging Spawn: Converging spawn threads 9021 are executed when athread transitions from callable mode back to workgroup mode. Aconverging spawn's arguments are a per-lane FFTID, and a mask indicatingwhether or not the lane's stack is empty. This mask must be computeddynamically by checking the value of the per-lane stack pointer at thereturn site. The compiler must compute this mask because these callablethreads may invoke each other recursively. Lanes in a converging spawnwhich do not have the convergence bit set will behave like ordinaryspawns.

Bindless thread dispatch solves the SIMD divergence issue for raytracing in some implementations which do not allow shared local memoryor barrier operations. In addition, in one embodiment of the invention,BTD is used to address SIMD divergence using a variety of computemodels. In particular, any compute dispatch with a thread group barrierand shared local memory can spawn bindless child threads (e.g., onechild thread at a time per parent) and all the same threads can beregrouped and dispatched by BTD for better efficiency. This embodimentallows the originating threads to share their shared local memory spacewith their child threads. The shared local memory allocations andbarriers are released only when finally converged parents terminate (asindicated by end of thread (EOT) indicators). One embodiment of theinvention also provides for amplification within callable mode, allowingtree traversal cases with more than one child being spawned.

Although not so limited, one embodiment of the invention is implementedon a system where no support for amplification is provided by any SIMDlane (i.e., allowing only a single outstanding SIMD lane in the form ofdiverged or converged spawn thread). In addition, in one implementation,the 32b of (FFTID, BARRIER_ID, SLM_ID) is sent to the BTD-enableddispatcher 8950 upon dispatching a thread. In one embodiment, all thesespaces are freed up prior to launching the threads and sending thisinformation to the bindless thread dispatcher 8950. Only a singlecontext is active at a time in one implementation. Therefore, a roguekernel even after tempering FFTID cannot access the address space of theother context.

In one embodiment, if StackID allocation is enabled, shared local memoryand barriers will no longer be dereferenced when a thread terminates.Instead, they are only dereferenced if all associated StackIDs have beenreleased when the thread terminates. One embodiment preventsfixed-function thread ID (FFTID) leaks by ensuring that StackIDs arereleased properly.

In one embodiment, barrier messages are specified to take a barrier IDexplicitly from the sending thread. This is necessary to enablebarrier/SLM usage after a bindless thread dispatch call.

FIG. 91 illustrates one embodiment of an architecture for performingbindless thread dispatching and thread/workgroup preemption as describedherein. The execution units (EU) 8910 of this embodiment support directmanipulation of the thread execution mask 9150-9153 and each BTD spawnmessage supports FFTID reference counting for re-spawning of a parentthread following completion of converging spawn 9021. Thus, the raytracing circuitry described herein supports additional message variantsfor BTD spawn and TraceRay messages. In one embodiment, the BTD-enableddispatcher 8950 maintains a per-FFTID (as assigned by thread dispatch)count of original SIMD lanes on diverging spawn threads 9001 and countsdown for converging spawn threads 9021 to launch the resumption of theparent threads 9000.

Various events may be counted during execution including, but notlimited to, regular spawn 9011 executions; diverging spawn executions9001; converging spawn events 9021; a FFTID counter reaching a minimumthreshold (e.g., 0); and loads performed for (FFTID, BARRIER_ID,SLM_ID).

In one embodiment, shared local memory (SLM) and barrier allocation areallowed with BTD-enabled threads (i.e., to honor ThreadGroup semantics).The BTD-enabled thread dispatcher 8950 decouples the FFTID release andthe barrier ID release from the end of thread (EOT) indications (e.g.,via specific messages).

In one embodiment, in order to support callable shaders from computethreads, a driver-managed buffer 9170 is used to store workgroupinformation across the bindless thread dispatches. In one particularimplementation, the driver-managed buffer 9170 includes a plurality ofentries, with each entry associated with a different FFTID.

In one embodiment, within the state initializer 8920, two bits areallocated to indicate the pipeline spawn type which is factored in formessage compaction. For diverging messages, the state initializer 8920also factors in the FFTID from the message and pipelines with each SIMDlane to the ray/box traversal block 8930 or bindless thread dispatcher8950. For converging spawn 9021, there is an FFTID for each SIMD lane inthe message and pipeline FFTID with each SIMD lane for the ray/boxtraversal unit 8930 or bindless thread dispatcher 8950. In oneembodiment, the ray/box traversal unit 8930 also pipelines the spawntype, including converging spawn 9021. In particular, in one embodiment,the ray/box traversal unit 8930 pipelines and stores the FFTID withevery ray converging spawn 9021 for TraceRay messages.

In one embodiment, the thread dispatcher 8950 has a dedicated interfaceto provide the following data structure in preparation for dispatching anew thread with the bindless thread dispatch enable bit set:

  Struct tsl_sts_inf { // non-stallable interface    Logic[8] FFTID;   Logic[8] BARRIER_ID;    Logic[8] SLM_ID;    Logic[8]count_valid_simd_lanes;   }

The bindless thread dispatcher 8950 also processes the end of thread(EOT) message with three additional bits: Release_FFTID,Release_BARRIER_ID, Release_SLM_ID. As mentioned, the end of thread(EOT) message does not necessarily release/dereference all theallocations associated with the IDs, but only the ones with a releasebit set. A typical use-case is when a diverging spawn 9001 is initiated,the spawning thread produces an EOT message but the release bit is notset. Its continuation after the converging spawn 9021 will produceanother EOT message, but this time with the release bit set. Only atthis stage will all the per-thread resources be recycled.

In one embodiment, the bindless thread dispatcher 8950 implements a newinterface to load the FFTID, BARRIER_ID, SLM_ID and the lane count. Itstores all of this information in an FFTID-addressable storage 9121 thatis a certain number of entries deep (max_fftid, 144 entries deep in oneembodiment). In one implementation, the BTD-enabled dispatcher 8950, inresponse to any regular spawn 9011 or diverging spawn 9001, uses thisidentifying information for each SIMD lane, performs queries to theFFTID-addressable storage 9121 on a per-FFTID basis, and stores thethread data in the sorting buffer as described above (see, e.g., contentaddressable memory 4201 in FIG. 42 ). This results in storing anadditional amount of data (e.g., 24 bits) in the sorting buffer 4201 perSIMD lane.

Upon receiving a converging spawn message, for every SIMD lane from thestate initializer 8920 or ray/box traversal block 8930 to the bindlessthread dispatcher 8950, the per-FFTID count is decremented. When a givenparent's FFTID counter becomes zero, the entire thread is scheduled withoriginal execution masks 9150-9153 with a continuation shader record4201 provided by the converging spawn message in the sorting circuitry4008.

Different embodiments of the invention may operate in accordance withdifferent configurations. For example, in one embodiment, all divergingspawns 9001 performed by a thread must have matching SIMD widths. Inaddition, in one embodiment, a SIMD lane must not perform a convergingspawn 9021 with the ConvergenceMask bit set within the relevantexecution mask 9150-9153 unless some earlier thread performed adiverging spawn with the same FFTID. If a diverging spawn 9001 isperformed with a given StackID, a converging spawn 9021 must occurbefore the next diverging spawn.

If any SIMD lane in a thread performs a diverging spawn, then all lanesmust eventually perform a diverging spawn. A thread which has performeda diverging spawn may not execute a barrier, or deadlock will occur.This restriction is necessary to enable spawns within divergent controlflow. The parent subgroup cannot not be respawned until all lanes havediverged and reconverged.

A thread must eventually terminate after performing any spawn toguarantee forward progress. If multiple spawns are performed prior tothread termination, deadlock may occur. In one particular embodiment,the following invariants are followed, although the underlyingprinciples of the invention are not so limited:

-   -   All diverging spawns performed by a thread must have matching        SIMD widths.    -   A SIMD lane must not perform a converging spawn with the        ConvergenceMask bit set within the relevant execution mask        9150-9153 unless some earlier thread performed a diverging spawn        with the same FFTID.    -   If a diverging spawn is performed with a given stackID, a        converging spawn must occur before the next diverging spawn.    -   If any SIMD lane in a thread performs a diverging spawn, then        all lanes must eventually perform a diverging spawn. A thread        which has performed a diverging spawn may not execute a barrier,        or deadlock will occur. This restriction enables spawns within        divergent control flow. The parent subgroup cannot not be        respawned until all lanes have diverged and reconverged.    -   A thread must eventually terminate after executing any spawn to        guarantee forward progress. If multiple spawns are performed        prior to thread termination, deadlock may occur.

In one embodiment, the BTD-enabled dispatcher 8950 includes threadpreemption logic 9120 to preempt the execution of certain types ofworkloads/threads to free resources for executing other types ofworkloads/threads. For example, the various embodiments described hereinmay execute both compute workloads and graphics workloads (including raytracing workloads) which may run at different priorities and/or havedifferent latency requirements. To address the requirements of eachworkload/thread, one embodiment of the invention suspends ray traversaloperations to free execution resources for a higher priorityworkload/thread or a workload/thread which will otherwise fail to meetspecified latency requirements.

As described above with respect to FIGS. 52A-B, one embodiment reducesthe storage requirements for traversal using a short stack 5203-5204 tostore a limited number of BVH nodes during traversal operations. Thesetechniques may be used by the embodiment in FIG. 91 , where the ray/boxtraversal unit 8930 efficiently pushes and pops entries to and from theshort stack 5203-5204 to ensure that the required BVH nodes 5290-5291are available. In addition, as traversal operations are performed,traversal/stack tracker 5248 updates the tracking data structure,referred to herein as the tracking array 5249, as well as the relevantstacks 5203-5204 and ray tracing data 4902. Using these techniques, whentraversal of a ray is paused and restarted, the traversalcircuitry/logic 8930 can consult the tracking data structure 5249 andaccess the relevant stacks 5203-5204 and ray tracing data 4902 to begintraversal operations for that ray at the same location within the BVHwhere it left off.

In one embodiment, the thread preemption logic 9120 determines when aset of traversal threads (or other thread types) are to be preempted asdescribed herein (e.g., to free resources for a higher priorityworkload/thread) and notifies the ray/box traversal unit 8930 so that itcan pause processing one of the current threads to free resources forprocessing the higher priority thread. In one embodiment, the“notification” is simply performed by dispatching instructions for a newthread before traversal is complete on an old thread.

Thus, one embodiment of the invention includes hardware support for bothsynchronous ray tracing, operating in workgroup mode (i.e., where allthreads of a workgroup are executed synchronously), and asynchronous raytracing, using bindless thread dispatch as described herein. Thesetechniques dramatically improve performance compared to current systemswhich require all threads in a workgroup to complete prior to performingpreemption. In contrast, the embodiments described herein can performstack-level and thread-level preemption by closely tracking traversaloperation, storing only the data required to restart, and using shortstacks when appropriate. These techniques are possible, at least inpart, because the ray tracing acceleration hardware and execution units8910 communicate via a persistent memory structure 3198 which is managedat the per-ray level and per-BVH level.

When a Traceray message is generated as described above and there is apreemption request, the ray traversal operation may be preempted atvarious stages, including (1) not yet started, (2) partially completedand preempted, (3) traversal complete with no bindless thread dispatch,and (4) traversal complete but with a bindless thread dispatch. If thetraversal is not yet started, then no additional data is required fromthe tracking array 5249 when the raytrace message is resumed. If thetraversal was partially completed, then the traversal/stack tracker 5248will read the tracking array 5249 to determine where to resumetraversal, using the ray tracing data 4902 and stacks 5121 as required.It may query the tracking array 5249 using the unique ID assigned toeach ray.

If the traversal was complete, and there was no bindless threaddispatch, then a bindless thread dispatch may be scheduled using any hitinformation stored in the tracking array 5249 (and/or other datastructures 4902, 5121). If traversal completed and there was a bindlessthread dispatch, then the bindless thread is restored and execution isresumed until complete.

In one embodiment, the tracking array 5249 includes an entry for eachunique ray ID for rays in flight and each entry may include one of theexecution masks 9150-9153 for a corresponding thread. Alternatively, theexecution masks 9150-9153 may be stored in a separate data structure. Ineither implementation, each entry in the tracking array 5249 may includeor be associated with a 1-bit value to indicate whether thecorresponding ray needs to be resubmitted when the ray/box traversalunit 8930 resumes operation following a preemption. In oneimplementation, this 1-bit value is managed within a thread group (i.e.,a workgroup). This bit may be set to 1 at the start of ray traversal andmay be reset back to 0 when ray traversal is complete.

The techniques described herein allow traversal threads associated withray traversal to be preempted by other threads (e.g., compute threads)without waiting for the traversal thread and/or the entire workgroup tocomplete, thereby improving performance associated with high priorityand/or low latency threads. Moreover, because of the techniquesdescribed herein for tracking traversal progress, the traversal threadcan be restarted where it left off, conserving a significant processingcycles and resource usage. In addition, the above-described embodimentsallow a workgroup thread to spawn a bindless thread and providesmechanisms for reconvergence to arrive back to the original SIMDarchitecture state. These techniques effectively improve performance forray tracing and compute threads by an order of magnitude.

Apparatus and Method for Data-Parallel Ray Tracing

In scientific visualization (but also in movies and other domains) datasets are increasingly growing to sizes that cannot be processed by asingle node. For off-line algorithms (mostly in movies) this is oftenhandled through paging, caching, and out-of-core techniques; but when aninteractive setting is required (e.g., visualization for oil-and-gas,scientific visualization in a large-data/HPC environment, interactivemovie content previews, etc) this is no longer possible. In this case,it is absolutely necessary to use some form of data parallel rendering,in which the data is partitioned across multiple different nodes—suchthat the entirety of the data can be stored across all nodes—and wherethese nodes collaborate in rendering the required image.

The embodiments of the invention include an apparatus and method forreducing the bandwidth for transferring rays and/or volume blocks in thecontext of data-distributed ray tracing across multiple compute nodes.FIG. 92 , for example, illustrates a ray tracing cluster 9200 comprisinga plurality of ray tracing nodes 9210-9213 which perform ray tracingoperations in parallel, potentially combining the results on one of thenodes. In the illustrated architecture, the ray tracing nodes 9210-9213are communicatively coupled to a client-side ray tracing application9230 via a gateway 9220.

It will be assumed in the description below that multiple nodes9210-9213 jointly hold the ray tracing data. Each such node 9210-9213may contain one or more CPUs, GPUs, FPGAs, etc, and the computations maybe performed on either individual ones or a combination of theseresources. In one embodiment, the compute nodes 9210-9213 communicatewith one another through some form of network 9215 such as Infiniband,OmniPath, or NVLink, to name a few. The data may be portioned across thememories of these nodes 9210-9213, either because the application usingthe renderer has itself partitioned the data (as is the case for many insitu algorithms or parallel middlewares such as ParaView, Visit, etc),or because the renderer has created this partitioning.

To do parallel rendering in such an environment, there are a variety ofalgorithmic choices: Compositing based approaches have each node rendersan image of its local data, and combine these partial results usingdepth- and/or alpha compositing. Data Forwarding (or caching) approachescompute a given ray's (or pixel's, path's, etc) operations on a givennode, detect whenever this ray/pixel/path needs data that lives onanother node, and fetches this data on demand. Ray Forwarding basedapproaches do not forward data to the rays that need it, and insteadsend the rays to where the data is: when a node detects that a ray needsprocessing with another node's data it sends that ray to the node owningthat data.

Among these choices, compositing is the simplest, and most widely used;however, it is only applicable for relatively simple rendering effects,and cannot easily be used for effects such as shadows, reflections,ambient occlusion, global illumination, volumetric scattering,volumetric shadows, etc. Such effects, which are being required morefrequently by users, require some sort of ray tracing in which case dataparallel rendering either fetches data to the rays or sends the rays tothe data. Both approaches have been used before and their limitationsare well understood. In particular, both approaches suffer from highbandwidth requirements, either by sending up to billions of rays around(for ray forwarding), or by having each node 9210-9213 fetch up to manygigabytes of data (for data forwarding), or both (when a combination ofboth is used).

Though network bandwidth is rising dramatically, data size and/or raycount are rising, too, meaning that this bandwidth is, in practice, veryquickly the limiting factor for performance. In fact, it often is thesole reason that interactive performance cannot be achieved, except invery simplistic settings (such as primary-ray only rendering, in whichcase one could also have used compositing).

One embodiment of the invention focuses on the core idea that, inpractice, very large parts of the data often do not actually matter fora given frame. For example, in volume rendering the user often uses a“transfer function” to highlight certain regions of the data, with lessinteresting data set to fully transparent. Clearly, a ray that wouldonly traverse “un-interesting” data would not need to fetch this data(or be sent to this data), and the respective bandwidth may be saved.Similarly, for surface-based ray tracing, if a ray passes through aregion of space owned by another node, but does not actually intersectany triangles there, then it does not actually need to interact withthis other node's triangles.

One embodiment extends the concepts of “empty space skipping” and“bounding volumes” from individual nodes to data parallel rendering, inthe form of using what are referred to herein as “proxies” 9230-9233 fora node's data. In particular, each node computes a very low memoryfootprint proxy 9230-9233 of its own data such that this proxy providesthe ability to either approximate or conservatively bound this data. Allnodes 9210-9213 then exchange their proxies 9230-9233 such that eachnode has every other node's proxies. For example, the proxies 9230stored on node 9210 will include proxy data from nodes 9211-9213. When anode needs to trace a ray through a spatial region owned by another nodeit first traces this ray through its own copy of this node's proxy. Ifthat proxy guarantees that no meaningful interaction will occur, it canskip sending that ray/fetching that data, thereby saving the bandwidthrequired for doing so.

FIG. 93 illustrates additional details of a ray tracing node 9210 inaccordance with one embodiment of the invention. A volume subdivisionmodule 9265 subdivides a volume into a plurality of partitions each ofwhich is processed by a different node. Working data set 9360 comprisesthe data for the partition to be processed by node 9210. A proxygeneration module 9250 generates a proxy 9340 based on the working dataset 9360. The proxy 9340 is transmitted to each of the other ray tracingnodes 9211-9213 which use the proxy to cull unneeded data as describedherein. Similarly, proxies 9341-9343 generated on nodes 9211-9213,respectively, are transmitted to node 9210. Ray tracing engine 9315performs ray tracing operations using both the working data set 9360stored locally and the proxies 9341-9343 provided by each of theinterconnected nodes 9211-9213.

FIG. 94 illustrates an example in which, in the context of volumerendering, a given volume data set 9400 is too large to be rendered onone node, so it gets partitioned into multiple blocks 9401-9404 (in thiscase, a 2×2 set). As illustrated in FIG. 95 , this logically partitionedvolume may then be distributed across different nodes 9210-9213, eachone retaining part of the volume.

Traditionally, every time a node wants to send a ray that passes throughother nodes' spatial regions, it has to either send this ray to thosenodes, or fetch those nodes' data. In FIG. 96 , for example, node 9210traces a ray that passes through space owned by nodes 9211-9213.

As illustrated in FIG. 97 , in one embodiment, each node 9210-9213computes a local proxy 9240-9243 for its part of the data 9401-9404,respectively, where the proxy is any sort of object that is(significantly) smaller in size, but allows for approximating orconservatively bounding that node's data. For example, in oneembodiment, each node computes what is commonly known as a “macrocellgrid”; a lower resolution grid where each cell corresponds to a regionof cells in the input volume, and where each such cell stores, forexample, the minimum and maximum scalar value in that region (in thecontext of single-node rendering, this is commonly used for “spaceskipping”). In the illustrated example, each node 9210-9213 computes onesuch proxy 9240-9243 for its part of the data. In one embodiment, allnodes then exchange their respective proxies until each node has allproxies for every node, as illustrated in FIG. 98 .

If, for a given transfer function setting, only some of the data valuesare actually interesting (in the sense that they are not completelytransparent), then this can be conservatively detected in the proxy(just as traditional, single-node space skipping does). This isillustrated as regions 9940-9943 in FIG. 99 .

In addition, since every node has every other nodes' proxies each nodecan also conservatively bound which other nodes' regions are interestingbased on the proxies it has for these nodes, as shown in FIG. 100 . Ifnode 9210 has to trace a ray that straddles node's 9211-9213's dataregions then the ray may be projected onto the proxy and traversedthere, as indicated by the dotted arrow. This indicates that though theray does pass through space owned by nodes 9210-9212, only node 9212actually contains any interesting regions, so this ray can be forwardedto node 9212, as indicated in FIG. 100 by the solid arrow, withoutprocessing on nodes 9210 or sending to node 9211 (or, in a cachingcontext, data may be fetched only from node 9210 rather than from both9211 and 9212).

A method in accordance with one embodiment of the invention isillustrated in FIG. 101 . The method may be implemented within thecontext of the architectures described above, but is not limited to anyparticular processing or system architecture.

At 10101, a volume is logically subdivided into a plurality ofpartitions (N) and, at 10102, data associated with the N partitions isdistributed to N different nodes (e.g., one partition per node in oneembodiment). At 10103, each node computes a proxy for its respectivepartition and sends the proxy to the other nodes. At 10104,traversal/intersection operations are performed for a current ray orgroup of rays (e.g., a beam) using the proxies, potentially ignoringcertain regions within the proxies which are not relevant to theoperations. As mentioned, for a given transfer function setting, onlysome of the data values are actually interesting (e.g., because they arenot completely transparent). This may be conservatively detected in theproxy as done with single-node space skipping. If the ray interacts withthe proxy, determined at 10105, then at 10106, the ray(s) are sent tothe node associated with the proxy or data is retrieved from the node.The next ray or group of rays are then selected at 10107.

Apparatus and Method for BVH Construction with Stochastic Processing

Top-down construction of bounding volume hierarchies (BVHs) (e.g., usingbinned surface area heuristic (SAH) operations) requires repeated memorytransactions to access all primitives (at least once per constructedlevel), which incurs a high bandwidth and computational overhead. Thistypically leaves the construction of the first levels of the hierarchyto be bandwidth-bound due to the sheer number of primitives that areprocessed. Limiting or reducing the number of such primitives translatesdirectly in a performance improvement for these types of BVH builders.

Approaches other than top-down construction trade build time withquality (ray tracing performance) of the hierarchy. Previous work hasfound that top-down approaches which minimize SAH exhibit better raytracing performance than other approaches.

Embodiments of the invention described below provide an extension totop-down construction which improves its performance while retainingacceptable quality (e.g., reduced by 5%-10%).

Some embodiments of the invention perform the following sequence ofoperations:

-   -   1. Pre-processing: The primitives are ordered spatially (Morton        ordering). This ordering is needed for the subset and insertion        phases.    -   2. Subset sampling: To ensure robustness, a stratified subset is        sampled over the spatially ordered primitives. This ensures that        no region is underrepresented in the subset. Additionally, the        selection probabilities of each primitive are proportional to a        measure, such as their bounding box diagonals over the area.        This ensures that large primitives are more likely to be in the        subset. Under stratification, large primitives are guaranteed to        be sampled. For efficiency, the sampling probabilities may be        clamped to the size of a sampling stratum. Otherwise, large        primitives could be sampled many times, resulting in compute        inefficiencies to reach the right subset size or a smaller        effective subset size.    -   3. Subset BVH construction: With a BVH builder, referred to as        the “interior constructor”, a BVH is constructed from the subset        data.    -   4. Insertion: The remaining primitives are inserted into the        leaves of the subset BVH. For each primitive, a search is        performed for the best leaf according to the smallest increase        in SAH cost. For efficiency, not all leaves are considered, but        only the spatially ordered neighborhood.    -   5. Cluster BVH construction: For each cluster, BVH construction        is continued using the interior constructor. In this instance,        however, a direct application is not optimal, since the        construction needs to be efficiently parallelized over all        clusters. A specific implementation is therefore suggested to        get the best performance.

Some embodiments do not require the data to be spatially ordered, thusalleviating the potentially expensive sorting step. This for examplealso helps in cases where data-streaming is more important, or the fulldata cannot fit in memory. One such embodiment operates as follows:

-   -   1. Subset sampling: Because preprocessing is not performed in        this sequence, the subset is sampled directly from the input        data (instead of from the spatially ordered data).    -   2. Subset BVH construction: As described above, a BVH is        constructed from the subset data.    -   3. Insertion: Primitives are inserted by performing a top-down        search in the tree, minimizing a cost metric like the increase        in SAH cost. The increase can be used as a bound to prune entire        subtrees (e.g., if a leaf with lower cost has already been        found). For optimization, each subtree may contain the smallest        of its leaf surface area, so that some search branches can be        cut due to higher overall SAH cost.    -   4. Cluster BVH construction: As described above for the first        sequence of operations. For each cluster, BVH construction is        continued using the interior constructor. In this instance,        however, a direct application is not optimal, since the        construction needs to be efficiently parallelized over all        clusters. A specific implementation is therefore suggested to        get the best performance.

Initial results, as illustrated in FIG. 102 , show that embodiments ofthe invention outperform the binned SAH builder in terms of performanceby up to two times on large scenes (sanmiguel, 10M primitives) whileretaining the same build quality. On the other hand, smaller scenes(such as crytek sponza, 300K primitives) do not benefit from theseembodiments, likely due to the overhead from the additional processingphases. Some embodiments reduce this overhead with further optimizationsand/or hardware with lower dispatch overhead.

Being able to fully rebuild the BVH more often instead of refitting,lowering the amount of data that needs to be processed on the GPU, orallowing for lazy builds can be beneficial and open new possibilities.These embodiments leverage the fact that a carefully selected subset ofthe data can give a good approximation of the final BVH, especiallyconcerning the upper topology that is the most expensive to process infull.

This approach allows representative geometry to be selected thatdescribes the data distribution spatially and cost-wise. After theapproximated tree is built from this subset, the rest of the geometry isinserted by carefully selecting the leaves to provide an optimal result.The leaves are further processed to obtain a full BVH. This, inconjunction with data streaming, compression and quantization candrastically improve performance.

The sheer amount of memory and bandwidth requirements to construct a BVHcan easily become a bottleneck both for real-time and interactiveapplications where dynamic geometry or out-of-core data are involved. Tooffset the building cost, which can represent a substantial fraction ofthe frame time, the tree is often refitted (e.g., via bottom-up updatesof bounding boxes) for a long time during animation before beingrebuilt. This has the effect of lowering its quality and increasing thetraversal cost. Reducing the cost of high-quality BVH construction isthus a useful goal, as it would allow higher quality BVHs to bemaintained under real-time dynamic conditions, improving at the sametime the ray traversal efficiency and the level of dynamic content thatcan be handled.

As mentioned, these embodiments leverage the fact that a carefullyselected subset of the data can give a good approximation of the finalBVH, especially concerning the upper topology that is the most expensiveto process in full. These embodiments can be used to improve hardwareBVH builders, which can struggle to build the first BVH levels due tothe memory footprint, and reach parallel build for sub-treessubstantially earlier. These embodiments can also enable real-timerendering to build a better quality BVH more often to avoid refittingwhich heavily degrades traversal performance and allow for constructionof huge scenes not possible before on normal hardware and/or to allowfor more detail in the resulting images.

A Survey on Bounding Volume Hierarchies for Ray Tracing. ComputerGraphics Forum 40, 2 (2021), 683-712 describes a full overview of BVHswhile Monte Carlo and QMC methods are described in Monte Carlo andQuasi-Monte Carlo Methods (Springer 1st ed.). Concerning stochasticmethods applied to BVH construction, the only known approach is arandomized plane splitting decision as described in Kelvin Ng andBorislav Trifonov, Automatic bounding volume hierarchy generation usingstochastic search methods (mini-workshop) (2003). Space partitioning hasbeen exploited in the form of grid based as described in Grid-based SAHBVH construction on a GPU, The Visual Computer 27, 6 (2011), 697-706 andIngo Wald, On fast construction of SAH-based bounding volume hierarchies(2007). Morton-sorted approaches can be used for faster build timesand/or two-stages builds as described in J. Pantaleoni and D. Luebke.2010. HLBVH: Hierarchical LBVH Construction for on High PerformanceGraphics, Eurographics Association, 87-95. Some embodiments rely onMorton code sorting, from which a representative subset of theprimitives can be importance sampled, creating the top hierarchy.

Encoding extra information into the Morton code bits to help withconstruction, like triangle size, has shown improvements over classicLBVH-like builders as described in Extended Morton codes for highperformance bounding volume hierarchy construction, Proceedings of highperformance graphics (2017), 1-8. The insertion stage can also be seenas an agglomerative approach, where the leaves of the stochastic BVHrepresent clusters of interest. Ploc is a bottom-up approach thataggregates pairs of triangles until the full hierarchy is built[Parallel Locally-Ordered Clustering for Bounding Volume HierarchyConstruction. IEEE Trans. Vis. Comput. Graph. 24, 3 (2018), 1345-1353],while k-means algorithms have been tested for fixed values of k[Parallel BVH construction using k-means clustering. The Visual Computer32, 6 (2016), 977-987]. In contrast, embodiments of the invention avoidexpensive data-sweeps to build from the bottom-up and are not limited toa fixed number of k nodes/clusters: these embodiments instead adaptdynamically to the input geometry. There are refining techniques thattake a full BVH and optimize it in a second pass: a LBVH is used as abase and further refined [Parallel BVH construction using progressivehierarchical refinement, Computer Graphics Forum, Vol. 36 (2017),487-494] or its nodes are shuffled to optimize its SAH cost[Fastinsertion-based optimization of bounding volume hierarchies. In ComputerGraphics Forum, Vol. 32 (2013), 85-100 and Parallel Reinsertion forBounding Volume Hierarchy Optimization, Computer Graphics Forum 37, 2(2018), 463-473]. It is also possible to build the tree incrementally byinserting one node at a time [Incremental BVH construction for raytracing. Computers & Graphics 47 (2015), 135-144 and Automatic creationof object hierarchies for ray tracing. IEEE Computer Graphics andApplications 7, 5 (1987), 14-20].

Different approaches to BVH construction provide specific trade-offsbetween algorithmic complexity and final tree quality. Fast Mortonbuilders usually come with a nk complexity from their radix sort, wherek is the key length and n the number of primitives, but suffer fromlower SAH quality. Top-down builders, instead, have a more expensive nlog n/m complexity, but give better SAH quality and thus fasterintersection tests. This section focuses on the latter case.

Embodiments of the invention subdivide BVH build complexity into threemain components: the subset BVH creation (1), the cluster BVHsconstruction (2) and the extra steps required for the full process (3).In (1) only a subset M of the data are used, bringing the complexity toO(m log m); after the upper part is built, the cluster BVHs construction(2) needs to iterate over n primitives and each tree has on average n/melements: this can be expressed as O(n log n/m). Finally, the overhead(3) of these embodiments touches n primitives if the spatial sorting isenabled, m during the sampling and n−m in the insertion step; in thiscase α∈[0, 1] is used as a factor relative to the construction time foroverhead and conservatively bound (3) to O (an log n).

By considering the construction time alone, an upper bound on thespeedup increase can be evaluated by the ratio between a normal top-downbuilder and the sum of the two construction steps (1,2) as

$\frac{n\log n}{{m\log m} + {n{\log\left( {n/m} \right)}}}.$

As shown in FIGS. 103A-B, depending on the amount of primitives used forthe subset M, there is a wide theoretical speedup ranging from 1× to 5×:while the trend is somewhat similar, almost plateauing after a fewhundred thousand primitives, there is a slow but steady increaseinversely proportional to the amount of data used. Since this amountalso affects the final quality of the BVH, in the next experiment areasonable size of 20% primitives is considered, that already provides atheoretical 3.5× speedup.

When adding also the overhead (3) for bounding the theoretical gain, therelative cost introduced compared to the pure building time isconsidered: this results in the speedup ratio

$\frac{n\log n}{{m\log m} + {n{\log\left( {n/m} \right)}} + {\alpha n\log n}}.$

To this end, it can be seen that α∈[0, 1] behaves with different numbersof primitives ranging from tens of thousands to tens of millions (FIGS.103A-B). As it is apparent, the overhead needs to be at most 60% of thebuild time of a standard top-down builder; after that, any theoreticalgain is lost. Another insight from this experiment suggests that thetechniques described here are largely independent from the number ofprimitives involved.

To summarize, there is a theoretical gain of up to 3× by using theembodiments described herein compared to a standard top-down builder.This result holds true as long as the builders have similarperformances. Given different hardware and software implementations,however, there might be other factors to consider.

FIGS. 103A-B show a theoretical analysis of construction time speed-uprelative to a top-down builder: the upper bound gain by different sizedsubsets in the construction time without overhead (a) is readily reachedafter a few hundred thousand primitives, and increases very slowlyafterwards. With overhead and a 20% subset size (b), the theoreticalupper bound gain decreases and breaks even when the extra computereaches 60% of the construction time, without much variation withincreasing number of primitives (colored lines, from 1e5 to 1e8).

Some embodiments include sequential stages as illustrated in FIG. 104 .A subset sampler 10402 generates a small representative subset of theinput primitives 10401 through stochastic importance sampling (e.g.,Subset Sampling, described below). From this subset, a subset BVHbuilder 10403 constructs an initial BVH (e.g., Subset BVH, describedbelow). For this, pre-existing BVH construction techniques can be used,referred to as the interior builder.

Insertion hardware logic 10404, the remaining primitives are insertedinto the leaves of the subset BVH that effectively operate as clusters.A cluster BVH builder 10405 continues the BVH construction in parallelfrom each of these clusters (e.g., cluster BVHs, described below). It isimportant to note that any builder can be used in this framework, but inparticular top-down builders like binned SAH will benefit the most. Thisis due to the higher bandwidth and compute demand from the upper part ofthe tree construction, that is mitigated from a smaller subset use.During the first step, some embodiments can also include an optionalspatial reordering for the primitives according to a space filling curve(i.e. Morton).

The various components illustrated in FIG. 104 may be implemented inhardware, software, or any combination thereof. For example, thehardware components may include dedicated fixed-function hardwarecomponents and general purpose instruction processing hardwarecomponents to execute program code to implement the techniques describedherein.

1. Subset Sampling

For the subset selection, embodiments identify a representativeselection of the primitives, leveraging an optional spatial ordering aswell as stratified and weighted sampling. Stratification and spatialordering ensure a stratified selection of the primitives in space.Weighted sampling allows steering of the selection towards moreimportant (larger) primitives, since it is beneficial to have themhigher up in the tree to allow for more efficient early ray termination.If those would be inserted only later in the insertion phase, they wouldend up in a lower level of the tree, resulting in bigger bounding boxesoverlaps and increased intersection costs.

The bounding box diagonal is used as the sampling weight to construct aCDF, from which we then sample M unique primitives. Some primitivesmight have a very large weight relative to the others, which wouldresult in a very inefficient sampling by being selected multiple times.Weights clamping is used to ensure that important primitives will stillbe selected but only once. Intuitively the normalization factor willredistribute the mass among the unclamped primitives, creating aniterative cycle. A conservative binned approach is used that splits thedata and selects the best clamp available.

The unknown spatial distribution of the primitives can result in denselytessellated tiny regions that are under-sampled and thus create unevensized leaves in the tree. To alleviate this issue, some uniformity inthe weights distribution can be re-introduced.

The Subset Sampling step can be divided in four phases: an optionalsorting of the primitives, importance sampling of the primitives,weights clamping, and some re-weighting to a more uniform distribution.The two latter phases happen before the sampling and are meant as analgorithmic optimization rather than a theoretical improvement.

To ensure guaranteed selection of large primitives, importance samplingof primitives is combined with stratified sampling. The sampling isguided by the size of the primitive. In particular one embodiment usesthe length of its bounding box diagonal.

When selecting which primitives to use to create the approximate BVH,the primitives that have a higher influence are importance sampled. Inthis context, selection is biased towards larger primitives whichbounding boxes would span considerably over space, instead of simplysampling uniformly over the data. To this end, some embodiments use aCumulative Density Function (CDF) over a properly chosen measure andsample the primitives from it: appropriate measures would be Area andthe Diagonal of the bounding box enclosing the primitive. This approachnot only helps later on in the insertion stage, but guarantees thatlarge bounding boxes stay at the top of the tree, where they can beevaluated early on. This not only gives an earlier ray termination, butdiminishes the overall traversal costs by avoiding large nodescontaining such primitives deeper in the tree.

The resulting selection will favor primitives that have a higher weight,without neglecting others that are still important to properly representthe full data distribution.

Since distribution in 3D space is also relevant, a way to reach allregions covered by the geometry is needed. This ensures that the extentof the approximated BVH resembles the correct one. A possible approachis to divide the space into strata and guarantee that we draw a samplefrom each of those by means of stratified sampling.

Primitives with probabilities larger than the stratum size areguaranteed to be sampled. However, large primitives can also covermultiple or even many strata, resulting in many duplicate samples. Thisreduces the efficiency of the sampling procedure. Prior to sampling theprimitives, their weights are clamped, such that the resulting samplingprobabilities are not (much) larger than the stratum size.

The quality and efficiency of the sampling procedure is greatly affectedby large primitives. Their sampling probabilities might be so large thatthey would be sampled many times, resulting in a smaller actual subsetsize. This is completely implementation dependent and connected with theM′ and rng. New samples could be drawn until the target subset size isreached, but the running time would be completely data-dependent(instead of only dependent on the size). To solve this issue, weights ofprimitives are clamped just enough so that very large primitives arestill guaranteed to be sampled at least once under stratified sampling.The clamped sampling probability of a primitive is defined as

${p_{i} = \frac{\min\left( {w_{i},c} \right)}{{\sum}_{j}{\min\left( {w_{j},c} \right)}}},$

where wi is the sampling weight of a primitive and c is the clampingweight. Since the focus is the sampling guarantee of large primitives,only the clamped sampling probability is considered

$p^{c} = \frac{c}{{\sum}_{j}{\min\left( {w_{j},c} \right)}}$

Algorithm 1: Sweep-based weight clamping input :Array W filled withweights to clamp Stratum size s output :Clamping weight S ←sortAscending(W); uSum ← 0; cSum ← |W|; // unclamped sum for w ϵ S do // clamped sum | if w ≥ s (uSum + w · cSum) then | |_ return uSum/(1/s− cSum); | uSum ← uSum + w; |_ cSum ← cSum − 1; return ∞;

To guarantee sampling of large primitives, the clamped samplingprobability only needs to be equal to or larger than the stratum size s(e.g. s=1/M), resulting in the following condition:

$c \geq {s{\sum\limits_{j}{\min\left( {w_{j},c} \right)}}}$

Successful evaluation for a given clamping weight means that largeprimitives are still guaranteed to be sampled at least once understratified sampling with a given stratum size s. Generally, the goal isto find the smallest (optimal) c to ensure that as few duplicate samplesoccur as possible. Obviously, directly computing the optimal clampingweight is difficult, given that the sum is dependent on it. The sum maytherefore be split into a sum of clamped and unclamped weights:

$c \geq {s\left( {{\sum\limits_{j,{w_{j} < c}}w_{j}} + {c{\sum\limits_{j,{w_{j} \geq c}}1}}} \right)}$

A sweep-based algorithm (Algorithm 1) is defined which evaluates eachweight as a possible clamping weight in ascending order. To do so, bothsums are incrementally updated during the sweep. Once a weight satisfiesthe clamping condition, the exact clamping weight must lie between thecurrent and previous weight. Since the sums do not change in this range,we can compute the exact clamping weight by rearranging the split sum:

$c^{\prime} = \frac{{\sum}_{j,w_{j < c}}w_{j}}{{1/s} - {{\sum}_{j,{w_{j} \geq c}}1}}$

While this algorithm allows to compute the exact clamping weight, it isalso relatively expensive, since it requires a sorting of the weights.Similar to binned BVH construction, binning can be used for weights andprimitive counts, to avoid the sorting step (Algorithm 2). The clampingcondition can then be evaluated on all bin boundaries and the smallestvalid boundary used as the clamping weight. We are primarily interestedin clamping large outlier weights, therefore we use exponentiallygrowing bins of base b to efficiently cover varying scales of weights.The resulting

Algorithm 2: Binned weight clamping input :Array W filled with weightsto clamp Stratum size s and bin base b, count c and offset o output:Clamping term binCounts ← [0|i ϵ [0,c)]; binSums ← [0|i ϵ [0,c)]; for wϵ W do // binning of weights | bin ← min(max(o + └log_(b) w,┘ 0), c −1); | binCounts[bin] ← binCounts[bin] + 1; |_ binSums[bin] ←binSums[bin] + w; uSum ← 0:  // unclamped sum cSum ← |W|;   // clampedsum for i ← 0 to c − 1 do    // bin search | clamp ← b^(i−0+1); | ifclamp ≥ s · (uSum + clamp · cSum) then | |_ return clamp; | uSum ←uSum + binSums [i]; |_ cSum ← cSum − binCounts [i]: return ∞;clamping weight will in general be larger than the optimal clampingweight c, but it is bounded by a factor of b (excluding cases where c isin the first or last bin). This is obvious if we consider the case wherec is just marginally above a bin boundary. Since this boundary does notsatisfy the clamping condition, the next boundary has to be selected,which is b-times larger than the previous boundary.

2. Iterative & Binned Weight Clamping

An issue with the sampling procedure can be seen when approaching asubset fraction of 100% or when many primitives have a relative largearea. The problem is that some elements with a huge weight in the CDFare constantly sampled, hindering the process of selecting M uniqueprimitives, or that the ones left to reach M have a very low samplingprobability. Some embodiments resolve this issue by clamping the weightsof the primitives in such a way that they would still have the sameprobability to be part of the subset (given stratified sampling),without skewing the sample distribution.

Given M stratified samples, the clamped probability that a primitive x∈Xwith a corresponding weight w_(x)∈W is selected exactly once is:

${P_{s}(x)} = {\min\left( {\frac{w_{x}}{{\sum}_{i}^{❘W❘}w_{i}},\frac{1}{M}} \right)}$

since we want to have M unique primitives, it follows that allnormalized weights are bigger than 1/M can be safely clamped. Let'sdefine the sets A and B to divide the primitives that do not needclamping from the others that do or are exactly equal to it.

$A = \left\{ {x \in X} \middle| {\frac{w_{x}}{{\sum}_{i \in X}w_{i}} < \frac{1}{M}} \right\}$B = {x ∈ X|x ∉ A}

The total sum weight S can be rewritten as

$S = {{\sum\limits_{x \in X}w_{x}} = {{\sum\limits_{a \in A}w_{a}} + {\sum\limits_{b \in B}w_{b}}}}$

to obtain the new clamped sum S′ as

$1 = {\frac{\sum_{a \in A}w_{a}}{S^{\prime}} + {{❘B❘}\frac{1}{M}}}$$S^{\prime} = {{\sum\limits_{a \in A}w_{a}} + {{❘B❘}\frac{S^{\prime}}{M}}}$${S^{\prime}\left( {1 - \frac{❘B❘}{M}} \right)} = {\sum\limits_{a \in A}w_{a}}$$S^{\prime} = \frac{\sum_{a \in A}w_{a}}{1 - \frac{❘B❘}{M}}$

It is easy to see that S′ is always non-negative since either the numberof elements in B is less than M or the set A must be empty. In thelatter case, the are M elements with exactly the same weight and wedon't need to clamp. Conversely if B=Ø then S′=S.

Intuitively we have re-scaled the weight of the primitives in A toaccount for the excess weight of the ones in B. While this solved theprevious issue, now it is possible that some elements in A should beclamped as well if

$\frac{W_{a}}{S^{\prime}} \geq {\frac{1}{M}.}$

This leads to an iterative approach.

If S_(i)′ is the clamped sum at iteration i of the algorithm, it issufficient to reach a fixed point where S_(i)′=S_(i+1)′.

The number of iteration scales with the size of M and the distributionof the weights: if the latter ones are very different in value, manyiterations may be needed as more and more of the bigger weights in Awill reach the clamping condition. Similarly, selecting a subset closeto the whole set will converge towards a uniform distribution in manysteps. To avoid the need of iterating multiple times, a binning approachmay be used that can give a good conservative result in just oneiteration.

Assuming to know S, let B_(i) ^(c) and B_(i) ^(s) be number and totalweight sum of the elements in the ith of k bins respectively, plus B_(i)^(min) and B_(i) ^(max) the minimum and maximum weight assigned to it,s. t. B_(i) ^(min)≤B_(i) ^(max)<B_(i+1) ^(min) ∀w_(x)∈B_(i). Then, ifeither

$B_{i}^{\min} \geq {\frac{S}{M}{or}B_{i}^{\max}} \geq \frac{S}{M}$

(the clamping condition):

$S_{A} = {\sum\limits_{j = 0}^{i - 1}B_{j}^{s}}$$C_{B} = {\sum\limits_{j = i}^{k}B_{j}^{c}}$$S^{\prime} = \frac{S_{A}}{1 - \frac{C_{B}}{M}}$

In general, S is not known from the start and thus we can't construct Aand B. A possible approach consists on binning all the primitives whileconstructing the Morton codes or building the CDF. Since the span of theweights are not known, a power histogram is used to bin the weights.After a prefix sum over the bins giving the value of S′ for each binnedinterval, the first bin that satisfies the clamping condition can beidentified and a conservative approximation of S′ computed (seeAlgorithm 2).

To ensure that densely tessellated regions are not underrepresented, theclamped sampling probabilities are mixed with uniform probabilities.Uniform sampling effectively importance samples dense regions. To retainthe sampling guarantee of large primitives, slight modifications to theweight clamping procedure are performed.

The mixture probability is

${p_{i}^{*} = {{u \cdot \frac{1}{N}} + {\left( {1 - u} \right) \cdot \frac{\min\left( {w_{i},c} \right)}{{\sum}_{j}{\min\left( {w_{j},c} \right)}}}}},$

where u is the uniform fraction. Using this mixture directly with theclamped probabilities would destroy the guarantee of sampling largeprimitives. In the same fashion as in the previous section, the clampedsampling probability needs to be larger than the stratum size s. Solvingfor c leaves us with:

$c \geq {\frac{s - {u/N}}{1 - u}{\sum\limits_{j}{\min\left( {w_{j},c} \right)}}}$

This equation differs from only in the factor before the sum. It can beextracted as an updated stratum size s′:

$s^{\prime} = \frac{s - {u/N}}{1 - u}$

s′ can be used in place of s in the previous section when a uniformmixture is used when sampling. Intuitively, the stratum size isincreased such that the resulting clamping weight reserves additionalweight to large primitives. After applying the uniform mixture, thisadditional weight is then redistributed among all primitives, leavinglarge primitives with just enough weight to fully cover the actualstratum size s (therefore still being guaranteed to be sampled).Obviously, some large primitives lose the guarantee of being sampled,namely the ones whose weight is between the previous and new clampingweight.

3. Subset BVH Construction

The upper topology is constructed using the representative subset. Onekey point in this process is recognizing that each of these primitivesrepresents a larger amount, so it has to be treated in a statisticalsense. We scale the bbox and cost of each selected primitive by theinverse of its selection probability

$\left( {\max\left( {\frac{1}{{P(x)}M},1} \right)} \right),$

and modify the SAH cost metric to account for it. This way smallerprimitives that are part of a finer mesh better approximate theirsurroundings, while large triangles that cover huge portions of the CDF(and, consequently, geometry) do not skew the overall topology.

4. Insertion

After the subset BVH has been constructed using the interior BVHconstructor, the remaining primitives now need to be inserted into theleaves of the subset BVH in order to continue the construction process.The leaves of the subset BVH are referred to as clusters. The problemdefinition of this phase is to associate each of the remainingprimitives with a cluster. The insertion decisions are guided by a costmodel as detailed below. This cost model is minimized by evaluatingmultiple candidate leaves for insertion and selecting the one with thelowest cost. The algorithm is detailed below.

The SAH cost of an existing BVH can be minimized by performingtopological rearrangements. Instead of computing the SAH directly for apossible rearrangement, only its absolute change can be computed bysubtracting the previous bounding box surface areas from the updatedones. Minimizing the change obviously gives the same result asminimizing the SAH directly. But it has the added advantage in that onlythe affected nodes have to be considered (the change is zero for allother nodes). This can lead to a more efficient algorithm and furtherpotential for optimizations. We apply this approach to the insertiondecision in the same way. Contrary to them, we are not performingrearrangements to the topology, but we insert new primitives into theleaves of the topology instead.

Given a primitive with bounding box B, we define the cost of insertingthis primitive into a leaf node L as

${{c_{B}(L)} = {{{{SA}\left( {L\bigcup B} \right)}\left( {{❘L❘} + 1} \right)} - {{{SA}(L)}{❘L❘}} + {\sum\limits_{N\epsilon\tau{(L)}}{{SA}\left( {N\bigcup B} \right)}} - {{SA}(N)}}},$

where τ(L) is the trail of nodes that lead from the root node to theleaf node L. The cost model captures the increase of the bounding boxareas along the trail and the leaf itself. We also incorporate theaddition of the primitive to the leaf. This proved to be crucial,otherwise primitives are attracted to large leaves which cover thementirely.

FIG. 105 illustrates an example of window construction of the pruningMorton window search. Each primitive 10503 possesses an index into acompacted array of subset primitives 10502. This array containsreferences to the leaf node 10501 each subset primitive 10502 belongsto. For a primitive 10504 this can be used to generate a window aroundits previous subset primitive in the compacted array (elements 80-87 ofthe subset primitives 10502). Each leaf node pointed to in the window isconsidered for insertion (highlighted leaf nodes 10501).

The flattened SAH metric expresses the cost of a (sub)tree with rootnode N as

${\mathcal{C}(N)} = {\frac{1}{{SA}(N)}\left\lbrack {{\mathcal{C}_{T}{\sum\limits_{N_{l}}{{SA}\left( N_{i} \right)}}} + {\mathcal{C}_{I}{\sum\limits_{N_{i}}{{{SA}\left( N_{l} \right)}{❘N_{l}❘}}}}} \right\rbrack}$

where SA is the surface area of internal (N_(i)) or leaf (N_(i)) nodes,and C_(T), C₁ the traversal and primitive intersection costsrespectively. The cost increase of inserting a new primitive p in thetree can be formulated by the individual increases at a leaf (I_(l)) andinternal node (I_(i)) level:

I(p,N)=I _(i)(p,N)+I _(l)(p,N)

where

$\begin{matrix}{{I_{I}\left( {p,N} \right)} = {C_{I}\left( {{{{SA}\left( N_{l}^{\prime} \right)}{❘{N_{l} + 1}❘}} - {{{SA}\left( N_{I} \right)}{❘N_{l}❘}}} \right)}} \\{= {C_{I}\left( {{{{SA}\left( {N_{l}^{\prime} - N_{l}} \right)}{❘N_{l}❘}} + {{{SA}\left( N_{l}^{\prime} \right)}❘}} \right.}}\end{matrix}$

similarly, the trail of parent nodes Np would increase their costaccordingly as

${I_{i}\left( {p,N} \right)} = {\mathcal{C}_{T}{\sum\limits_{N_{p}}{{SA}\left( {N_{p}^{\prime} - N_{p}} \right)}}}$

Evaluating the cost function for each leaf node for each primitive wouldbe too expensive in practice. Spatial ordering of primitives isleveraged in order to perform a localized search for insertioncandidates (FIG. 105 ). The algorithm considers a fixed number of subsetprimitives and their leaf nodes around a primitive. Since subsetprimitives are sparsely represented in the original array, the leafpointers are stored into a separate compacted array. A prefix sum isused to perform this compaction, which is also used to later index intothis compacted array.

Algorithm 3: Pruning morton window search input :Primitive index|i,bounding box bBox^(P) and window size w output :Leaf node to insertprimitive into m ← closestSubset Primitive [i]; minCost ← ∞; for j ϵ [m− w, m + w] do | leaf ← subsetPrimitiveCluster[j]; | bBox ← getBBox(node); | bBox* ← merge (bBox, bBox^(P)); | c ← getPrimCount (leaf); |cost ← area (bBox^(P)) · (c+1) − area (bBox) · c; | node ← getParent(leaf); | diffCost ← 1; | while node ≠ ⊥ ∧ cost < minCost ∧ diff Cost >0 do | | bBox ← getBBox (node); | | bBox* ← merge (bBox, bBox^(P)); | |diffCost ← area (bBox*) − area (bBox); | | cost ← cost + diffCost; | |_node ← getParent(node); | if cost < minCost then | | minCost ← cost; |_|_ minNode ← leaf; return minNode;

One embodiment of a search is detailed in algorithm 3. The leaf with thesmallest cost is tracked and an iteration performed over the window. Foreach candidate, the cost is computed by traversing the tree upwardstowards the parent. The cost is simply the sum of surface area changesof the traversed inner nodes. The fact that all cost terms are positiveis exploited, i.e., while traversing towards the parent, the cost canonly increase. When a leaf has already been found with a smaller costthan the cost we have computed so far for the candidate leaf, thetraversal can be aborted early and this node not considered any further.

Terminology. In the following, the terminology from OpenCL is used torefer to the GPU programming model. A work group (CUDA: Thread block) isa set of threads that can directly synchronize with each other and alsoexchange data through fast but limited shared memory. Work groups arefurther subdivided into sub groups (CUDA: Warp). Threads inside a subgroup are executed in lockstep and can directly exchange data throughregister permutations.

Framework. In some embodiments, the algorithm is implemented both as aCPU-builder in pbrt and as a GPU-builder based on one API DPC++. The CPUbuilder was used to test various approaches that lead to the finaldesign. The GPU-builder implements a subset of the functionality presentin the CPU-builder and is used for performance testing and comparison toother build algorithms. Unless explicitly stated, the followingparagraphs refer to the GPU implementation.

Subset Sampling. A direct implementation of the binned weight clampingprocedure (Algorithm 2) posed to be one order of magnitude slower thanexpected. To solve this issue, the algorithm is further simplified insome embodiments, by only tracking primitive counts per bin, instead ofalso the sum of their weights. The sum is bounded by the maximum rangein the bin times the primitive count. The error of this approximation isa factor of b, where b is the bin base. Together with the error ofevaluating the clamping condition on bin boundaries only, the combinederror bound for the resulting clamped sampling probability increases tob2. A smaller base b′=√b can be used to get the same error bound bagain. This approach resulted in the expected performance.

In some embodiments, to sample the actual subset primitives, inverse CDFsampling is employed. After the weights have been computed, a prefix sumover them is computed, which is later used for a binary search with agiven random number. Single precision floating point numbers are usedfor the CDF. While for large scenes, the available precision is notenough to faithfully represent regions with low probability, it does notaffect the results much. However, rounding errors in the CDF do have aneffect. The prefix scan routine employed performs the scan across animplicit hierarchy with three levels. First, a scan per sub group isperformed, then a scan per work group and finally a scan over all workgroups. This approach exhibits good error properties, since it iscomparable to a pairwise summation (but wider).

In one embodiment, for random numbers, a Sobol random number builderand/or a binned SAH builder as the interior build algorithm. Forconstructing the subset BVH a three-staged approach can be employed toefficiently exploit the available parallelism. In the first stage,single nodes are processed by multiple work groups by distributing theirprimitives among them. After a fixed number of iterations, enough nodesbecome available to be independently processed by individual work groups(second stage). After a few more iterations, even more nodes becomeavailable such that they can be independently processed by individualsub groups (third stage). The third stage is executed as a single pass,where a sub group constructs the entire remaining subtree of a node.

While most construction algorithms can readily be used for constructingthe subset BVH (as long as the resulting BVH is not refined down to oneprimitive per node), the same does not hold true for the cluster BVHconstruction, where having to construct many small trees may become anissue. The naïve approach of reusing the algorithm from the subset BVHconstruction would be heavily inefficient. Instead, the fact that thenumber of clusters is typically already far higher than the number ofconcurrently executing sub groups is exploited. Consequently, a slightlymodified version of the third stage of the subset BVH constructionalgorithm above is used. Each sub group is therefore responsible toconstruct the entire subtree of a cluster. This is also the reason whyuniformity needs to be added to the subset sampling. Otherwise, largeclusters of highly tessellated geometry would overwhelm the processingcapabilities of individual sub groups, resulting in load imbalanceproblems.

Clustering. Slight modifications to the pruning Morton window search(Algorithm 3) can be performed to better exploit the parallelism in thehardware. Instead of having each thread traverse their own window, thewindows of a sub group are unified (by taking the min of the lower andthe max of the upper range). This results in perfectly coherent memoryaccess and lead to an improvement of roughly 20%, even though the actualwindow has increased for each thread. Additionally, it seems to bebeneficial to first evaluate the center of the window for each threadindependently.

While the memory access is relatively inefficient in that case, thefound node is oftentimes already the optimum. As such, pruning may betriggered earlier when the window is traversed. Re-checking nodes isavoided, since they can (and often are) referenced multiple times in thesame window. In can be sufficient to check if the node to be checked isthe same as the minimum found so far. This case in particular cannot bepruned by the cost bound. The run time with all of these optimizationsis reduced down to a third of the original run time.

Comparative Evaluation

In this section the embodiments of the invention are comparativelyevaluated against existing build algorithms targeted for GPUs. Wecompare against LBVH, ATRBVH, PLOC and binned SAH construction. Theimplementation of the latter is identical to the interior builder usedin some embodiments of the invention. Binary hierarchies are built. Raytracing performance is measured by converting the binary hierarchy to ahardware-dependent format first. The hardware format expects a maximumof eight leaves per primitive. For top-down builders, the constructionprocess is stopped early. Since the other builders construct hierarchiesthat are refined down to one primitive per leaf, the conversion prunesthe tree accordingly.

The LBVH implementation can be used to generate an initial topology. Theoptimization step is a port from the publicly available CUDAimplementation into the SYCL-Framework. The implementation whichallocates the distance matrix to local memory is ported. The CUDAimplementation makes heavy use of shuffle intrinsics to performreductions inside sub groups. In some cases, these can be replaced byhigher-level primitives provided by SYCL (e.g. sub_group_reduce_max). Arecommended treelet size of 9 is used in some embodiments and twoiterations are performed. A sub group size of 16 (instead of 32 on CUDA)is used in some embodiments.

Morton ordering of primitives has been shown to not significantly affectthe quality of the subset (inferred from a similar quality of the finalBVH). This makes the leaf nodes relatively stable, since they areessentially averages over multiple subset primitives. While the sortingdoes not seem to be relevant for the subset generation, it is veryrelevant for the following insertion step. The pruning Morton windowsearch has proven to be most efficient compared to the other approacheswe tested.

Embodiments were evaluated using the binned top-down builder as aninterior builder. We experimented with using the PLOC algorithm as aninterior builder, too, but without success. When ignoring the sortingstep, PLOC is essentially a linear-time algorithm. This is due to thedecreasing set size after each iteration. As a result, the higher levelsof the tree are already fast to compute. We therefore cannot expect anyspeedup when using these embodiments. This means that the algorithm isonly really applicable to algorithms that have roughly the same timecomplexity for each level, typically O(n log n). Some embodimentseffectively linearize the construction steps (although the entirealgorithm still remains in O(n log n)). [

In order to differentiate primitives in the Morton code, the number ofsymbols in the Morton code needs to follow a roughly logarithmicrelationship on the number of primitives. When the hierarchy on a subsetis first constructed, shorter Morton codes may be used resulting infewer sorting iterations. After insertion, each cluster would need to besorted too. But since the clusters are typically very small, evenshorter Morton codes can be used.

On a high level, the subset sampling phase selects a subset of M uniqueprimitives out of N input primitives (M<<N). The subset is then given tothe interior BVH constructor for higher construction speed of the firstlevels of the hierarchy. During experiments two constraints were notedwhich need to be upheld by the sampling procedure: (1) Large primitives(relative to the other primitives) must be part of the subset. (2) Denseregions must not be underrepresented in the subset. As for the firstconstraint, it is assumed that large primitives have a high impact onthe overall topology and their exclusion results in inferior BVHquality. The second constraint is only due to efficiency. When denselytessellated regions with many small primitives are underrepresented inthe subset, clusters with very large primitive counts were observed inthose regions. While this does not affect the quality of the final BVH,it creates load imbalance problems for the cluster BVH constructionphase. Both constraints are opposite with regards to each other(including small primitives means that less larger primitives can beincluded) and therefore need to be balanced.

Embodiments of the invention may include various steps, which have beendescribed above. The steps may be embodied in machine-executableinstructions which may be used to cause a general-purpose orspecial-purpose processor to perform the steps. Alternatively, thesesteps may be performed by specific hardware components that containhardwired logic for performing the steps, or by any combination ofprogrammed computer components and custom hardware components.

EXAMPLES

The following are example implementations of different embodiments ofthe invention.

Example 1. An apparatus comprising: a primitive sampler to identify arepresentative subset of input primitives of a graphics scene; boundingvolume hierarchy (BVH) builder hardware logic to construct anapproximate BVH based on the representative subset of input primitives;hardware logic to insert input primitives not in the representativesubset into leaves of the approximate BVH; and the BVH builder or adifferent BVH builder to construct a final BVH based on the primitivesinserted into the leaves of the approximate BVH.

Example 2. The apparatus of example 1 wherein the primitive samplercomprises an importance sampler to perform stochastic importancesampling to identify the representative subset of input primitives.

Example 3. The apparatus of example 1 wherein the BVH builder or thedifferent BVH builder is to operate in parallel on the leaves after theprimitives are inserted to construct the final BVH.

Example 4. The apparatus of example 1 further comprising: compressionhardware logic to perform compression and/or quantization on nodes ofthe final BVH to generate a compressed final BVH.

Example 5. The apparatus of example 2 wherein the importance sampler isto bias selection of the subset of input primitives to primitives thathave a greater influence on the approximate BVH.

Example 6. The apparatus of example 5 wherein the importance sampler isto bias selection of relatively larger primitives over relativelysmaller primitives.

Example 7. The apparatus of example 6 wherein the importance sampler isto implement a Cumulative Density Function (CDF) to identify the subsetof input primitives.

Example 8. The apparatus of example 1 further comprising: traversalhardware logic to traverse a ray through the final BVH; and intersectionhardware logic to identify intersections between the ray and one or moreof the input primitives.

Example 9. A method comprising: sampling input primitives of a graphicsscene to identify a representative subset of the input primitives;constructing an approximate bounding volume hierarchy (BVH) based on therepresentative subset of input primitives; inserting input primitivesnot in the representative subset into leaves of the approximate BVH; andconstructing a final BVH based on the primitives inserted into theleaves of the approximate BVH.

Example 10. The method of example 9 wherein sampling input primitivesfurther comprises performing stochastic importance sampling to identifythe representative subset of input primitives.

Example 11. The method of example 9 wherein constructing the final BVHfurther comprises operating in parallel on the leaves after theprimitives are inserted.

Example 12. The method of example 9 further comprising: performingcompression and/or quantization on nodes of the final BVH to generate acompressed final BVH.

Example 13. The method of example 10 selection of the subset of inputprimitives is biased to primitives that have a greater influence on theapproximate BVH.

Example 14. The method of example 13 wherein relatively largerprimitives are biased to be selected over relatively smaller primitives.

Example 15. The method of example 14 wherein a Cumulative DensityFunction (CDF) is implemented to identify the subset of inputprimitives.

Example 16. The method of example 9 further comprising: traversing a raythrough the final BVH; and identifying intersections between the ray andone or more of the input primitives.

Example 17. A machine-readable medium having program code stored thereonwhich, when executed by a machine, causes the machine to perform theoperations of: sampling input primitives of a graphics scene to identifya representative subset of the input primitives; constructing anapproximate bounding volume hierarchy (BVH) based on the representativesubset of input primitives; inserting input primitives not in therepresentative subset into leaves of the approximate BVH; andconstructing a final BVH based on the primitives inserted into theleaves of the approximate BVH.

Example 18. The machine-readable medium of example 17 wherein samplinginput primitives further comprises performing stochastic importancesampling to identify the representative subset of input primitives.

Example 19. The machine-readable medium of example 17 whereinconstructing the final BVH further comprises operating in parallel onthe leaves after the primitives are inserted.

Example 20. The machine-readable medium of example 17 further comprisingprogram code to cause the machine to perform the operations of:performing compression and/or quantization on nodes of the final BVH togenerate a compressed final BVH.

Example 21. The machine-readable medium of example 18 selection of thesubset of input primitives is biased to primitives that have a greaterinfluence on the approximate BVH.

Example 22. The machine-readable medium of example 21 wherein relativelylarger primitives are biased to be selected over relatively smallerprimitives.

Example 23. The machine-readable medium of example 22 wherein aCumulative Density Function (CDF) is implemented to identify the subsetof input primitives.

Example 24. The machine-readable medium of example 17 further comprisingprogram code to cause the machine to perform the operations of:traversing a ray through the final BVH; and identifying intersectionsbetween the ray and one or more of the input primitives.

As described herein, instructions may refer to specific configurationsof hardware such as application specific integrated circuits (ASICs)configured to perform certain operations or having a predeterminedfunctionality or software instructions stored in memory embodied in anon-transitory computer readable medium. Thus, the techniques shown inthe figures can be implemented using code and data stored and executedon one or more electronic devices (e.g., an end station, a networkelement, etc.). Such electronic devices store and communicate(internally and/or with other electronic devices over a network) codeand data using computer machine-readable media, such as non-transitorycomputer machine-readable storage media (e.g., magnetic disks; opticaldisks; random access memory; read only memory; flash memory devices;phase-change memory) and transitory computer machine-readablecommunication media (e.g., electrical, optical, acoustical or other formof propagated signals—such as carrier waves, infrared signals, digitalsignals, etc.).

In addition, such electronic devices typically include a set of one ormore processors coupled to one or more other components, such as one ormore storage devices (non-transitory machine-readable storage media),user input/output devices (e.g., a keyboard, a touchscreen, and/or adisplay), and network connections. The coupling of the set of processorsand other components is typically through one or more busses and bridges(also termed as bus controllers). The storage device and signalscarrying the network traffic respectively represent one or moremachine-readable storage media and machine-readable communication media.Thus, the storage device of a given electronic device typically storescode and/or data for execution on the set of one or more processors ofthat electronic device. Of course, one or more parts of an embodiment ofthe invention may be implemented using different combinations ofsoftware, firmware, and/or hardware. Throughout this detaileddescription, for the purposes of explanation, numerous specific detailswere set forth in order to provide a thorough understanding of thepresent invention. It will be apparent, however, to one skilled in theart that the invention may be practiced without some of these specificdetails. In certain instances, well known structures and functions werenot described in elaborate detail in order to avoid obscuring thesubject matter of the present invention. Accordingly, the scope andspirit of the invention should be judged in terms of the claims whichfollow.

We claim:
 1. An apparatus comprising: a primitive sampler to identify arepresentative subset of input primitives of a graphics scene; boundingvolume hierarchy (BVH) builder hardware logic to construct anapproximate BVH based on the representative subset of input primitives;hardware logic to insert input primitives not in the representativesubset into leaves of the approximate BVH; and the BVH builder or adifferent BVH builder to construct a final BVH based on the primitivesinserted into the leaves of the approximate BVH.
 2. The apparatus ofclaim 1 wherein the primitive sampler comprises an importance sampler toperform stochastic importance sampling to identify the representativesubset of input primitives.
 3. The apparatus of claim 1 wherein the BVHbuilder or the different BVH builder is to operate in parallel on theleaves after the primitives are inserted to construct the final BVH. 4.The apparatus of claim 1 further comprising: compression hardware logicto perform compression and/or quantization on nodes of the final BVH togenerate a compressed final BVH.
 5. The apparatus of claim 2 wherein theimportance sampler is to bias selection of the subset of inputprimitives to primitives that have a greater influence on theapproximate BVH.
 6. The apparatus of claim 5 wherein the importancesampler is to bias selection of relatively larger primitives overrelatively smaller primitives.
 7. The apparatus of claim 6 wherein theimportance sampler is to implement a Cumulative Density Function (CDF)to identify the subset of input primitives.
 8. The apparatus of claim 1further comprising: traversal hardware logic to traverse a ray throughthe final BVH; and intersection hardware logic to identify intersectionsbetween the ray and one or more of the input primitives.
 9. A methodcomprising: sampling input primitives of a graphics scene to identify arepresentative subset of the input primitives; constructing anapproximate bounding volume hierarchy (BVH) based on the representativesubset of input primitives; inserting input primitives not in therepresentative subset into leaves of the approximate BVH; andconstructing a final BVH based on the primitives inserted into theleaves of the approximate BVH.
 10. The method of claim 9 whereinsampling input primitives further comprises performing stochasticimportance sampling to identify the representative subset of inputprimitives.
 11. The method of claim 9 wherein constructing the final BVHfurther comprises operating in parallel on the leaves after theprimitives are inserted.
 12. The method of claim 9 further comprising:performing compression and/or quantization on nodes of the final BVH togenerate a compressed final BVH.
 13. The method of claim 10 selection ofthe subset of input primitives is biased to primitives that have agreater influence on the approximate BVH.
 14. The method of claim 13wherein relatively larger primitives are biased to be selected overrelatively smaller primitives.
 15. The method of claim 14 wherein aCumulative Density Function (CDF) is implemented to identify the subsetof input primitives.
 16. The method of claim 9 further comprising:traversing a ray through the final BVH; and identifying intersectionsbetween the ray and one or more of the input primitives.
 17. Amachine-readable medium having program code stored thereon which, whenexecuted by a machine, causes the machine to perform the operations of:sampling input primitives of a graphics scene to identify arepresentative subset of the input primitives; constructing anapproximate bounding volume hierarchy (BVH) based on the representativesubset of input primitives; inserting input primitives not in therepresentative subset into leaves of the approximate BVH; andconstructing a final BVH based on the primitives inserted into theleaves of the approximate BVH.
 18. The machine-readable medium of claim17 wherein sampling input primitives further comprises performingstochastic importance sampling to identify the representative subset ofinput primitives.
 19. The machine-readable medium of claim 17 whereinconstructing the final BVH further comprises operating in parallel onthe leaves after the primitives are inserted.
 20. The machine-readablemedium of claim 17 further comprising program code to cause the machineto perform the operations of: performing compression and/or quantizationon nodes of the final BVH to generate a compressed final BVH.
 21. Themachine-readable medium of claim 18 selection of the subset of inputprimitives is biased to primitives that have a greater influence on theapproximate BVH.
 22. The machine-readable medium of claim 21 whereinrelatively larger primitives are biased to be selected over relativelysmaller primitives.
 23. The machine-readable medium of claim 22 whereina Cumulative Density Function (CDF) is implemented to identify thesubset of input primitives.
 24. The machine-readable medium of claim 17further comprising program code to cause the machine to perform theoperations of: traversing a ray through the final BVH; and identifyingintersections between the ray and one or more of the input primitives.